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Background: Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. Methods: We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. Results: The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. Conclusion: The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach. [ABSTRACT FROM AUTHOR]

Healthcare demand is growing in Australia and across the world. In Australia, the healthcare system comprises a mix of private and public organizations, such as hospitals, clinics, and aged care facilities. The Australian healthcare system is quite affordable and accessible because a large proportion of the expenditure, around 68%, is funded by the Australian government. The healthcare expenditure in 2015-16 was AUD 170.4 billion which was 10.0% of the GDP. Soaring healthcare costs and growing demand for services are increasing the pressure on the sustainability of the government-funded healthcare system. To be sustainable, we need to be more efficient in delivering healthcare services. We can schedule the care delivery process optimally and subsequently improve the efficiency of the system if demand for services is well known. However, there is a randomness in demand for services, and it is a cause of inefficiency in the healthcare delivery process. Soaring healthcare costs and the growing demand for services require us to use healthcare resources more efficiently. Randomness in resource requirements makes the care delivery process less efficient. Our aim is to reduce the uncertainty in patients’ resource requirements, and we achieve that objective by classifying patients into similar resource user groups. The conventional random forest., k-nearest neighbourhood (KNN) methods were resulted in poor classification, prediction performance. In this work, we develop a two-stage classification model to classify patients into lower variability resource user groups by using electronic patient record. There are various statistical tools for classifying patients into lower variability resource user groups. However, classification and regression tree (CART) analysis is a more suitable method for analyzing healthcare data because it has some distinct features. For example, it can handle the interaction between predictor variables naturally, it is nonparametric in nature, and it is relatively insensitive to the curse of dimensionality. [ABSTRACT FROM AUTHOR]

Eragrostis curvula is an agronomically and ecologically undesirable perennial tussock grass dispersed across Australia. The objective of this study is to investigate relationships of ecologically relevant abiotic variables with the presence of E. curvula at a landscape scale in the Snowy Monaro region, Australia. Through vegetation surveys across 21 privately owned properties and freely available ancillary data on E. curvula presence, we used seven predictor variables, including Sentinel 2 NDVI reflectance, topography, distance from roads and watercourses and climate, to predict the presence or absence of E. curvula across its invaded range using a random forest (RF) algorithm. Assessment of performance metrics resulted in a pseudo-R squared of 0.96, a kappa of 0.97 and an R squared for out-of-bag samples of 0.67. Temperature had the largest influence on the model's performance, followed by linear features such as highways and rivers. Highways' high importance in the model may indicate that the presence or absence of E. curvula is related to the density of human transit, thus as a vector of E. curvula propagule dispersal. Further, humans' tendency to reside adjacent to rivers may indicate that E. curvula's presence or absence is related to human density and E. curvula's potential to spread via water courses. [ABSTRACT FROM AUTHOR]

• Innovative phenotype-genotype association analysis for categorical genomic data having non-ignorable missing values. • Statistical learning method of random forest is used for variable selection in the analysis. • Weighted logistic ridge regression with EM algorithm is used for missing data imputation in the analysis. • Linear statistical hypothesis testing is used for determining the missingness mechanism in the analysis. • An application to analyzing real data from Australia breast cancer genome-wide association study (GWAS). Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS. [ABSTRACT FROM AUTHOR]

Eragrostis curvula is an agronomically and ecologically undesirable perennial tussock grass dispersed across Australia. The objective of this study is to investigate relationships of ecologically relevant abiotic variables with the presence of E. curvula at a landscape scale in the Snowy Monaro region, Australia. Through vegetation surveys across 21 privately owned properties and freely available ancillary data on E. curvula presence, we used seven predictor variables, including Sentinel 2 NDVI reflectance, topography, distance from roads and watercourses and climate, to predict the presence or absence of E. curvula across its invaded range using a random forest (RF) algorithm. Assessment of performance metrics resulted in a pseudo-R squared of 0.96, a kappa of 0.97 and an R squared for out-of-bag samples of 0.67. Temperature had the largest influence on the model's performance, followed by linear features such as highways and rivers. Highways' high importance in the model may indicate that the presence or absence of E. curvula is related to the density of human transit, thus as a vector of E. curvula propagule dispersal. Further, humans' tendency to reside adjacent to rivers may indicate that E. curvula's presence or absence is related to human density and E. curvula's potential to spread via water courses. [ABSTRACT FROM AUTHOR]

Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence points and existing mineral prospectivity maps developed using the weights-of-evidence (WofE) method. The ability to capture training points accurately and the simplicity of the proposed approach make it advantageous over complex mineral prospectivity mapping methods, to serve as a preliminary evaluation technique. The methodology can be modified with different datasets and algorithms, facilitating the investigations of mineral prospectivity in other regions and providing guidance for more detailed, high-resolution geological investigations. [ABSTRACT FROM AUTHOR]

Background Kidneys donated after circulatory death suffer a period of functional warm ischaemia before death, which may lead to early ischaemic injury. Effects of haemodynamic trajectories during the agonal phase on delayed graft function (DGF) is unknown. We aimed to predict the risk of DGF using patterns of trajectories of systolic blood pressure (SBP) declines in Maastricht category 3 kidney donors. Methods We conducted a cohort study of all kidney transplant recipients in Australia who received kidneys from donation after circulatory death donors, divided into a derivation cohort (transplants between 9 April 2014 and 2 January 2018 [462 donors]) and a validation cohort (transplants between 6 January 2018 and 24 December 2019 [324 donors]). Patterns of SBP decline using latent class models were evaluated against the odds of DGF using a two-stage linear mixed effects model. Results In the derivation cohort, 462 donors were included in the latent class analyses and 379 donors in the mixed effects model. Of the 696 eligible transplant recipients, 380 (54.6%) experienced DGF. Ten different trajectories, with distinct patterns of SBP decline were identified. Compared with recipients from donors with the slowest decline in SBP after withdrawal of cardiorespiratory support, the adjusted odds ratio (aOR) for DGF was 5.5 [95% confidence interval (CI) 1.38–28.0] for recipients from donors with a steeper decline and lowest SBP [mean 49.5 mmHg (standard deviation 12.5)] at the time of withdrawal. For every 1 mmHg/min reduction in the rate of decline of SBP, the respective aORs for DGF were 0.95 (95% CI 0.91–0.99) and 0.98 (95% CI 0.93–1.0) in the random forest and least absolute shrinkage and selection operator models. In the validation cohort, the respective aORs were 0.95 (95% CI 0.91–1.0) and 0.99 (95% CI 0.94–1.0). Conclusion Trajectories of SBP decline and their determinants are predictive of DGF. These results support a trajectory-based assessment of haemodynamic changes in donors after circulatory death during the agonal phase for donor suitability and post-transplant outcomes. [ABSTRACT FROM AUTHOR]

Accurate pre-harvest yield forecasting of mango is essential to the industry as it supports better decision making around harvesting logistics and forward selling, thus optimizing productivity and reducing food waste. Current methods for yield forecasting such as manually counting 2–3% of the orchard can be accurate but are very time inefficient and labour intensive. More recent evaluations of technological solutions such as remote (satellite) and proximal (on ground) sensing have provided very encouraging results, but they still require infield in-season sampling for calibration, the technology comes at a significant cost, and commercial availability is limited, especially for vehicle-mounted sensors. This study presents the first evaluation of a "time series"—based remote sensing method for yield forecasting of mango, a method that does not require infield fruit counts and utilizes freely available satellite imagery. Historic yield data from 2015 to 2022 were sourced from 51 individual orchard blocks from two farms (AH and MK) in the Northern Territory of Australia. Time series measures of the canopy reflectance properties of the blocks were obtained from Landsat 7 and 8 satellite data for the 2015–2022 growing seasons. From the imagery, the following vegetation indices (VIs) were derived: EVI, GNDVI, NDVI, and LSWI, whilst corresponding weather variables (rainfall (Prec), temperature (Tmin/Tmax), evapotranspiration (ETo), solar radiation (Rad), and vapor pressure deficit (vpd)) were also sourced from SILO data. To determine the relationships among weather and remotely sensed measures of canopy throughout the growing season and the yield achieved (at the block level and the farm level), six machine learning (ML) algorithms, namely random forest (RF), support vector regression (SVR), eXtreme gradient boosting (XGBOOST), RIDGE, LASSO and partial least square regression (PLSR), were trialed. The EVI/GNDVI and Prec/Tmin were found to be the best RS and weather predictors, respectively. The block-level combined RS/weather-based RF model for 2021 produced the best result (MAE = 2.9 t/ha), marginally better than the RS only RF model (MAE = 3.4 t/ha). The farm-level model error (FLEM) was generally lower than the block-level model error, for both the combined RS/weather-based RF model (farm = 3.7%, block (NMAE) = 33.6% for 2021) and the RS-based model (farm = 4.3%, block = 38.4% for 2021). Further testing of the RS/weather-based RF models over six additional orchards (other than AH and MK) produced errors ranging between 24% and 39% from 2016 to 2020. Although accuracies of prediction did vary at both the block level and the farm level, this preliminary study demonstrates the potential of a "time series" RS method for predicting mango yields. The benefits to the mango industry are that it utilizes freely available imagery, requires no infield calibration, and provides predictions several months before the commercial harvest. Therefore, this outcome not only presents a more adoptable option for the industry, but also better supports automation and scalability in terms of block-, farm-, regional, and national level forecasting. [ABSTRACT FROM AUTHOR]

One of the most valuable and nutritionally essential agricultural commodities worldwide is milk. The European Union and New Zealand are the second- and third-largest exporting regions of milk products and rely heavily on pasture-based production systems. They are comparable to the Australian systems investigated in this study. With projections of herd decline, increased milk yield must be obtained from a combination of animal genetics and feed efficiencies. Accurate pasture biomass estimation across all seasons will improve feed efficiency and increase the productivity of dairy farms; however, the existing time-consuming and manual methods of pasture measurement limit improvements to utilisation. In this study, Sentinel-2 (S2) band and spectral index (SI) information were coupled with the broad season and management-derived datasets using a Random Forest (RF) machine learning (ML) framework to develop a perennial ryegrass (PRG) biomass prediction model accurate to +/−500 kg DM/ha, and that could predict pasture yield above 3000 kg DM/ha. Measurements of PRG biomass were taken from 11 working dairy farms across southeastern Australia over 2019–2021. Of the 68 possible variables investigated, multiple simulations identified 12 S2 bands and 9 SI, management and season as the most important variables, where Short-Wave Infrared (SWIR) bands were the most influential in predicting pasture biomass above 4000 kg DM/ha. Conditional Latin Hypercube Sampling (cLHS) was used to split the dataset into 80% and 20% for model calibration and internal validation in addition to an entirely independent validation dataset. The combined internal model validation showed R2 = 0.90, LCCC = 0.72, RMSE = 439.49 kg DM/ha, NRMSE = 15.08, and the combined independent validation had R2 = 0.88, LCCC = 0.68, RMSE = 457.05 kg DM/ha, NRMSE = 19.83. The key findings of this study indicated that the data obtained from the S2 bands and SI were appropriate for making accurate estimations of PRG biomass. Furthermore, including SWIR bands significantly improved the model. Finally, by utilising an RF ML model, a single 'global' model can automate PRG biomass prediction with high accuracy across extensive regions of all seasons and types of farm management. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R 2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R 2 = −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R 2 = 0.23) and canopy layers (R 2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions. [ABSTRACT FROM AUTHOR]

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (Vmax) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the Vmax at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall. [ABSTRACT FROM AUTHOR]

Population-level uptake of direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection, including retreatment, can be estimated through administrative pharmaceutical dispensation data. However, the reasons for retreatment are not captured in these data. We developed a machine learning model to classify retreatments as reinfection or treatment failure at a national level. Retreatment data from the REACH-C cohort (n = 10,843 treated with DAAs; n = 320 retreatments with known reason), were used to train a random forest model. Nested cross validation was undertaken to assess model performance and to optimise hyperparameters. The model was applied to data on DAA retreatment dispensed during 2016-2021 in Australia, to identify the reason for retreatment (treatment failure or reinfection). Average predictive accuracy, precision, sensitivity, specificity and F 1 -score for the model were 96.3%, 96.5%, 96.3%, 96.3% and 96.3%, respectively. Nationally, 95,272 individuals initiated DAAs, with treatment uptake declining from 32,454 in 2016 to 6,566 in 2021. Of those treated, 6,980 (7%) were retreated. Our model classified 51.8% (95% CI 46.7–53.6%; n = 3,614) of cases as reinfection and 48.2% (95% CI 46.4–53.3%; n = 3,366) as treatment failure. Retreatment for reinfection increased steadily over the study period from 14 in 2016 to 1,092 in 2020, stabilising in 2021. Retreatment for treatment failure increased from 73 in 2016 to 1,077 in 2019, then declined to 515 in 2021. Among individuals retreated for treatment failure, 50% had discontinued initial treatment. We used a novel methodology with high classification accuracy to evaluate DAA retreatment patterns at a national level. Increases in retreatment uptake for treatment failure corresponded to the availability of pangenotypic and salvage regimens. Increasing retreatment uptake for reinfection likely reflects increasing reinfection incidence. This study used machine learning methodologies to analyse national administrative data and characterise trends in HCV retreatment due to reinfection and treatment failure. Retreatment for reinfection increased over time, reflecting increasing numbers of people at risk for reinfection following HCV cure. Increased retreatment for treatment failure corresponded to the availability of pangenotypic and salvage DAA regimens. The findings of this study can be used by public health agencies and policy makers to guide and assess HCV elimination strategies, while the novel methodology for monitoring trends in HCV retreatment has the potential to be used in other settings, and health conditions. [Display omitted] • Seven percent of people treated for HCV in Australia were retreated. • Machine learning was used to classify retreatments based on national administrative data. • Overall, 52% of retreatment was for reinfection and 48% was for treatment failure. • Retreatment for reinfection increased over time, reflecting increasing numbers of people at-risk for reinfection following cure. • Retreatment for treatment failure increased when salvage DAAs became available. [ABSTRACT FROM AUTHOR]

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection. [ABSTRACT FROM AUTHOR]

Evapotranspiration by phreatophytes in riparian zones makes up a large component of the water balance. However, our understanding of the relative importance of controlling factors such as climatic conditions, species type, depth to groundwater and distance to surface water in riparian zones remains a significant knowledge gap. A field experiment was conducted in an irrigated catchment in North Queensland, Australia, to investigate the factors controlling evapotranspiration by groundwater dependent trees. The sap flow of four tree species was measured, along with soil moisture, groundwater levels and local climatic conditions. The relative influence of species, hydrologic and climate factors, and measured variables were investigated with two non-parametric methods: random forest and Principal Component Analysis (PCA). Field monitoring data revealed differences in sap flow rates and diurnal sap flow trends between species. Distance from surface water explained the most variance in sap flow rates, followed by depth to groundwater and species, based on random forest modeling. The sap flow rates for some of the Eucalyptus tessellaris trees at this site reduced as groundwater levels declined. Overall, results demonstrate the value that can be gained from applying non-parametric methods, such as random forest and PCA, to investigate the relative importance of the factors influencing evapotranspiration. [ABSTRACT FROM AUTHOR]

Summary: Landscape rehabilitation following mining is required to be resilient to disturbance impacts such as fire, drought and disease. As mining companies undergo the process of rehabilitation certification and mine closure, there are notable knowledge gaps on the ecological risks associated with mature rehabilitated landscapes, based largely on the assumption that rehabilitation is analogous to reference communities. However, the response to fire disturbance across a range of landscapes remains largely untested and in particular there is limited understanding of recovery traits of plant species that occur naturally or are commonly seeded into rehabilitation. In August 2018, a controlled fire was applied to 37 hectares of 12‐year‐old coal‐mine rehabilitation in central Queensland, Australia. We used a combination of (i) ground plot surveys and (ii) drone imagery to compare the vegetation response of burnt woody species to unburnt controls prior to, and for, two years following the fire. The survival of the most dominant shrub species found on the rehabilitation site was significantly impacted by the fire. Old Man Saltbush (Atriplex nummularia Lindl. subsp. nummularia) recorded significant post‐fire mortality, with ground surveys recording an average reduction of 89% of stems per hectare across the burnt site, while unburnt controls remained unchanged. The plot data analysis was supported with high spatial and temporal resolution drone imagery, classified using a Random Forest machine‐learning approach. Change analysis of these maps showed a significant decline of 82% in Old Man Saltbush plant density and 92% reduction in foliage cover following the fire. In addition, the mean canopy area of individual Old Man Saltbush shrubs reduced significantly from a pre‐fire mean of 11.3 to 4.8 m2 two years following the fire. A spatial proximity analysis showed that those individuals that survived the fire were located significantly closer to unburnt areas and bare spoil, indicating that discontinuous ground fuel loads can greatly improve the survivability of individuals. This study provides new evidence on the contested fire sensitivity of Old Man Salt bush and demonstrates the risk that future climate‐driven extreme events may have on the resilience of novel ecosystems. [ABSTRACT FROM AUTHOR]

Preventing unplanned hospitalisations, including readmissions and re-presentations to the emergency department, is an important strategy for addressing the growing demand for hospital care. Significant successes have been reported from interventions put in place by hospitals to reduce their incidence. However, there is limited use of data-driven algorithms in hospital services to identify patients for enrolment into these intervention programs. Here we present the results of a study aiming to develop algorithms deployable at scale as part of a state government's initiative to address rehospitalizations and which fills several gaps identified in the state-of-the-art literature. To the best of our knowledge, our study involves the largest-ever sample size for developing risk models. Logistic regression, random forests and gradient boosted techniques were explored as model candidates and validated retrospectively on five years of data from 27 hospitals in Queensland, Australia. The models used a range of predictor variables sourced from state-wide Emergency Department(ED), inpatient, hospital-dispensed medications and hospital-requested pathology databases. The investigation leads to several findings: (i) the advantage of looking at a longer patient data history, (ii) ED and inpatient datasets alone can provide useful information for predicting hospitalisation risk and the addition of medications and pathology test results leads to trivial performance improvements, (iii) predicting readmissions to the hospital was slightly easier than predicting re-presentations to ED after an inpatient stay, which was slightly easier again than predicting re-presentations to ED after an EDstay, (iv) a gradient boosted approach (XGBoost) was systematically the most powerful modelling approach across various tests. [ABSTRACT FROM AUTHOR]

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable's scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities. [ABSTRACT FROM AUTHOR]

The power of Machine Learning is demonstrated for automatic interpretation of well logs and determining reservoir properties for volume of shale, porosity, and water saturation respectively for tight clastic sequences. Random Forest algorithms are reputed for their efficiency as they belong to a class of algorithms called ensemble methods, which are traditionally seen as weak learners, but can be transformed into strong performers and they promise to deliver highly accurate results. The study area is located offshore Australia in the Poseidon and Crown fields situated in the Browse Basin, which are gas fields in tight complex clastic reservoirs. There are 5 wells used in this study with one well manually interpreted which is subsequently used in developing a machine learning model which predicts the output for the other 4 wells. The basic open hole logs namely Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, P-wave and S-wave sonic travel-time, are used in interpretation. One of the wells has a missing S-wave travel-time log which was also predicted by developing a Random Forest Machine Learning model. The results indicate a very robust improvement in performance when Random Forest algorithm was combined with Adaptive Boosting when interpreting the well logs. The training accuracy using Random Forest alone was 98.21%, but testing was 77.62% which suggested over-fitting by the Random Forest model. The Adaptive Boosting of the Random Forest algorithm resulted in the overall training accuracy of 99.40% and an overall testing accuracy of 97.03%, indicating a drastic improvement in performance. S-wave travel-time log was predicted by preparing a training set consisting of Natural gamma ray, Resistivity, Neutron Porosity, Bulk Density, and P-wave travel-time logs for the 4 wells using Random Forest which gave a training accuracy of 99.79% and a testing accuracy of 98.54%. Machine learning algorithms can be successfully applied for interpreting well log data in complex sedimentary environment and their performance can be drastically improved using Adaptive Boosting. [ABSTRACT FROM AUTHOR]

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a 5 dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. [ABSTRACT FROM AUTHOR]

Land cover mapping from satellite images has progressed from visual and statistical approaches to Random Forests (RFs) and, more recently, advanced image recognition techniques such as convolutional neural networks (CNNs). CNNs have a conceptual benefit over RFs in recognising spatial feature context, but potentially at the cost of reduced spatial detail. We tested the use of CNNs for improved land cover mapping based on Landsat data, compared with RFs, for a study area of approximately 500 km × 500 km in southeastern Australia. Landsat 8 geomedian composite surface reflectances were available for 2018. Label data were a simple nine-member land cover classification derived from reference land use mapping (Catchment Scale Land Use of Australia—CLUM), and further enhanced by using custom forest extent mapping (Forests of Australia). Experiments were undertaken testing U-Net CNN for segmentation of Landsat 8 geomedian imagery to determine the optimal combination of input Landsat 8 bands. The results were compared with those from a simple autoencoder as well as an RF model. Segmentation test results for the best performing U-Net CNN models produced an overall accuracy of 79% and weighted-mean F1 score of 77% (9 band input) or 76% (6 band input) for a simple nine-member land cover classification, compared with 73% and 68% (6 band input), respectively, for the best RF model. We conclude that U-Net CNN models can generate annual land cover maps with good accuracy from proxy training data, and can also be used for quality control or improvement of existing land cover products. [ABSTRACT FROM AUTHOR]

Almonds (Prunus dulcis Mill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis. [Display omitted] • Origin determination of almonds by lipidomics analysis via UPLC-ESI-IM-qToF-MS. • 250 almond samples from six different countries were used for analysis. • Random forest analysis achieved an accuracy of 88.8 %. • Metabolites were selected by Surrogate Minimal Depth and identified as triacylglycerols. [ABSTRACT FROM AUTHOR]

A basalt lava plain covers much of central and western Victoria in Australia. Many of the younger flows (<1 Ma) have hummocky surfaces with abundant tumuli and lava rises, known locally as stony rises due to the protruding basalt boulders. Stony rises are noteworthy geologically but have ecological importance and are culturally significant to Aboriginal Australians. Ecologically, stony rises host many native plant species. Culturally, stony rises provide evidence of Aboriginal land use and occupation through the presence of artefact deposits. Currently, mapping of stony rises is carried out by conducting field surveys on a study-by-study basis. This could be performed more rapidly using remote sensing datasets such as LiDAR, satellite and aerial imagery, and geophysical data. This research mapped stony rises in an area that is experiencing rapid urban development using a combination of remotely sensed datasets that characterise the morphology of stony rises through a Machine Learning approach that utilises Object Based Image Analysis (OBIA) segmentation and Random Forests (RF) classification techniques. The research successfully identified stony rises in the landscape to an accuracy of 89.97 %, with 314 potential new stony rise sites identified. The research identified the most important predictor variables were slope, local elevation, the concentrations of thorium and uranium (obtained from airborne radiometric data), aeromagnetic data (total magnetic intensity), the Normalised Difference Water Index (NDWI), and the Clay Mineral Ratio. The results from this study lay a foundation for standardising the definition of a stony rise within Australia. Furthermore, by utilising various predictor variables, this study highlights the success of a multi-faceted approach into detecting a specific landform at a landscape level. • Stony rises detected to an accuracy of 89.97 % using our novel method. • 314 potential new stony rises were detected using our novel method. • Using multifaceted predictor variables for landform classification and detection [ABSTRACT FROM AUTHOR]

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive demonstrably improves when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, 'C-Dax' reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed "SPECTRA-FOR" (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML. [Display omitted] • New method to optimise Sentinel-2 imagery using Planet Labs SuperDove data. • The near-infrared band is more sensitive for retrieving aboveground biomass. • The random forest algorithm can capture the grazing regimes of individual farms. • Model performance and evaluation rely on quality data and ample field observations. • The evaluation of the model with monthly data outperformed the varied temporal scales. [ABSTRACT FROM AUTHOR]

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms−1). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non‐developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non‐developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones. [ABSTRACT FROM AUTHOR]

There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible for farmers. The aim of this study was to fill this need and develop a novel approach to identify crop yield gaps through site- and season-specific models of crop yield potential. The study focused on cotton lint yield, with data from 14 different seasons and 68 different fields from a collection of large, irrigated cotton farms in eastern Australia. This abundance of yield data was then joined with other spatial and temporal datasets that describe yield, such as rainfall, temperature, soil, and management. A quantile random forest machine learning model was then used to model yield at 30 m resolution, where the 95th percentile predictions were treated as the yield potential. The yield gaps at a 30 m resolution were then estimated for all seasons and sites. The results were compared to a more traditional 'historical maximum yield' approach, where no data modelling and only empirical yield data was used to estimate the yield potential. This revealed that there was a general agreement between the two approaches, although the quantile machine learning approach is both site- and season-specific, not just site-specific. Overall, there is a great need for alternative approaches to estimate yield potential and yield gaps, as the approaches currently available possess many limitations. The approach developed in this study has the potential for wide-spread adoption in broadacre cropping systems, and if the causes of yield gaps are identified, could lead to the implementation of management strategies to close them. [ABSTRACT FROM AUTHOR]

Despite their size and contribution to the global carbon cycle, we have limited understanding of tropical savannas and their current trajectory with climate change and anthropogenic pressures. Here we examined interannual variability and externally forced long‐term changes in carbon and water exchange from a high rainfall savanna site in the seasonal tropics of north Australia. We used an 18‐year flux data time series (2001–2019) to detect trends and drivers of fluxes of carbon and water. Significant positive trends in gross primary productivity (GPP, 15.4 g C m2 year−2), ecosystem respiration (Reco, 8.0 g C m2 year−2), net ecosystem productivity (NEE, 7.4 g C m2 year−2) and ecosystem water use efficiency (WUE, 0.0077 g C kg H2O−1 year−1) were computed. There was a weaker, non‐significant trend in latent energy exchange (LE, 0.34 W m−2 year−1). Rainfall from a nearby site increased statistically over a 45‐year period during the observation period. To examine the dominant drivers of changes in GPP and WUE, we used a random forest approach and a terrestrial biosphere model to conduct an attribution experiment. Radiant energy was the dominant driver of wet season fluxes, whereas soil water content dominated dry season fluxes. The model attribution suggested that [CO2], precipitation and Tair accounting for 90% of the modelled trend in GPP and WUE. Positive trends in fluxes were largest in the dry season implying tree components were a larger contributor than the grassy understorey. Fluxes and environmental drivers were not significant during the wet season, the period when grasses are active. The site is potentially still recovering from a cyclone 45 years ago and regrowth from this event may also be contributing to the observed trends in sequestration, highlighting the need to understand fluxes and their drivers from sub‐diurnal to decadal scales. [ABSTRACT FROM AUTHOR]

Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks. [ABSTRACT FROM AUTHOR]

This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R2) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R2 of more than 90%. [ABSTRACT FROM AUTHOR]

Aim: Many species are adapted to a particular fire regime and major deviations from that regime may lead to localized extinction. Here, we quantify immaturity risks to an obligate‐seeder forest tree using an objectively designed climate model ensemble and a probabilistic fire regime simulator to predict future fire regimes. Location: Alpine ash (Eucalyptus delegatensis) distribution, Victoria, south‐eastern Australia. Methods: We used a fire regime model (FROST) with six climate projections from a climate model ensemble across 3.7 million hectares of native forest and non‐native vegetation to examine immaturity risks to obligate‐seeder forests dominated by alpine ash (Eucalyptus delegatensis), which has a primary juvenile period of approximately 20 years. Our models incorporated current and future projected climate including fuel feedbacks to simulate fire regimes over 100 years. We then used Random Forest modelling to evaluate which spatial characteristics of the landscape were associated with high immaturity risks to alpine ash forest patches. Results: Significant shifts to the fire regime were predicted under all six future climate projections. Increases in both wildfire extent (total area burnt, area burnt at high intensity) and frequency were predicted with an average increase of up to 110 hectares burnt annually by short‐interval fires (i.e., within the expected minimum time to reproductive maturity). The immaturity risk posed by short‐interval fires to alpine ash forest patches was well explained by Random Forest models and varied with both location and environmental variables. Main conclusions: Alpine ash forests are predicted to be burned at greater intensities and shorter intervals under future fire regimes. About 67% of the current alpine ash distribution was predicted to be at some level of immaturity risk over the 100‐year modelling period, with the greatest risks to those patches located on the periphery of the current distribution, closer to roads or surrounded by a drier landscape at lower elevations. [ABSTRACT FROM AUTHOR]

Background: The aims of this retrospective study were to (i) provide a description of sleep quality in elite athletes as measured by the Pittsburgh Sleep Quality Index (PSQI), (ii) provide normative PSQI data, (iii) identify differences across sex and sport, (iv) identify components that contribute to high PSQI scores and (v) assess PSQI test–retest reliability. Methods: The PSQI was completed by 479 athletes (371 female and 108 male) across 20 Olympic team and individual sports. For ordinal and categorical variables, the Wilcoxon rank sum test and Chi Squared tests were used, respectively. A random forest regression was built to determine the importance of each PSQI component. Test–retest reliability was assessed using two-way mixed effects intraclass correlation coefficients. Results: Fifty-two percent of athletes had a global PSQI score ≥ 5. Team sport athletes reported significantly longer sleep onset latency times but longer sleep durations compared with individual sport athletes. Sleep onset latency and sleep quality made the greatest contribution to the global PSQI scores. The PSQI demonstrated variability over periods of 2 months or more, with a minimal detectable change of 3 arbitrary units (AU). Conclusion: Long sleep onset latency and poor perceived sleep quality made the greatest contribution to the high PSQI scores observed in approximately half of elite athletes investigated. The PSQI should be administered at regular intervals due to variability within individuals over periods of 2 months or more. Individual questionnaire items or component scores of the PSQI may be useful for practitioners in guiding decision-making regarding sleep interventions in athletes. [ABSTRACT FROM AUTHOR]

The Great Barrier Reef World Heritage Area (GBRWHA) in north eastern Australia spans 2500 km of coastline and covers an area of ~ 350,000 km2. It includes one of the world's largest seagrass resources. To provide a foundation to monitor, establish trends and manage the protection of seagrass meadows in the GBRWHA we quantified potential seagrass community extent using six random forest models that include environmental data and seagrass sampling history. We identified 88,331 km2 of potential seagrass habitat in intertidal and subtidal areas: 1111 km2 in estuaries, 16,276 km2 in coastal areas, and 70,934 km2 in reef areas. Thirty-six seagrass community types were defined by species assemblages within these habitat types using multivariate regression tree models. We show that the structure, location and distribution of the seagrass communities is the result of complex environmental interactions. These environmental conditions include depth, tidal exposure, latitude, current speed, benthic light, proportion of mud in the sediment, water type, water temperature, salinity, and wind speed. Our analysis will underpin spatial planning, can be used in the design of monitoring programs to represent the diversity of seagrass communities and will facilitate our understanding of environmental risk to these habitats. [ABSTRACT FROM AUTHOR]

Understanding vegetation recovery after drought is critical for projecting vegetation dynamics in future climates. From 1997 to 2009, Australia experienced a long‐lasting drought known as the Millennium Drought (MD), which led to widespread reductions in vegetation productivity. However, vegetation recovery post‐drought and its determinants remain unclear. This study leverages remote sensing products from different sources—fraction of absorbed photosynthetically active radiation (FPAR), based on optical data, and canopy density, derived from microwave data—and random forest algorithms to assess drought recovery over Australian natural vegetation during a 20‐year period centered on the MD. Post‐drought recovery was prevalent across the continent, with 6 out of 10 drought events seeing full recovery within about 6 months. Canopy density was slower to recover than leaf area seen in FPAR. The probability of full recovery was most strongly controlled by drought return interval, post‐drought hydrological condition, and drought length. Full recovery was seldom observed when drought events occurred at intervals of 3 months or less, and moderately dry (standardized water balance anomaly [SWBA] within [−1, −0.76]) post‐drought conditions resulted in less complete recovery than wet (SWBA > 0.3) post‐drought conditions. Press droughts, which are long term but not extreme, delayed recovery more than pulse droughts (short term but extreme) and led to a higher frequency of persistent decline. Following press droughts, the frequency of persistent decline differed little among biome types but peaked in semi‐arid regions across aridity levels. Forests and savanna required the longest recovery times for press drought, while grasslands were the slowest to recover for pulse drought. This study provides quantitative thresholds that could be used to improve the modeling of ecosystem dynamics post‐drought. [ABSTRACT FROM AUTHOR]

Summary: Serrated Tussock (Nassella trichotoma) is a weed that invades native grasslands in southern Australia. It is frequently controlled using extensive and costly chemical control. We describe a tool aimed at grassland reserve managers that provides guidance about the suitability of different chemical control techniques, including knapsack, boom and aerial spraying. The tool takes the attributes of any site and predicts the suitability of each technique. The tool was built using random forests trained on a dataset of practitioner judgements. We discuss the applications and limitations of the tool. [ABSTRACT FROM AUTHOR]

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups—for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists. [ABSTRACT FROM AUTHOR]

Background: Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques.Methods: Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy.Results: During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers were a 'lack of ability to plan for change', 'lack of internal supporters for the change', 'lack of knowledge and experience', 'lack of monitoring and feedback', 'lack of individual alignment with the change', 'undefined change objectives', 'lack of objective feedback' and 'lack of time'. The random forest algorithm used was able to provide 96.9% prediction accuracy. The strategy category with the highest predicted resolution rate across the most number of implementation barriers was 'to empower stakeholders to develop objectives and solve problems'.Conclusions: Results from this study have provided a better understanding of implementation barriers in community pharmacy and how data-driven approaches can be used to predict the effectiveness of facilitation strategies to overcome implementation barriers. Tailored facilitation strategies such as these can increase the rate of real-time implementation of innovations in healthcare, leading to an industry that can confidently and efficiently adapt to continuous change. [ABSTRACT FROM AUTHOR]

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs. [ABSTRACT FROM AUTHOR]

· Mean annual stream temperature was found to warm by 1.00 °C over 1992–2021. · Increased air temperatures and urbanization are key drivers of warming stream temperatures. · Air temperature and urbanization together warm streams more than their individual effects alone. · Both local and global solutions are needed to reduce future increases to stream temperature. Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited. This study quantifies changes in stream temperatures in a region in South-East Australia with an urban-agricultural-forest landcover gradient and where increasing air temperatures have been observed. Using Random Forest models we identify air temperature and urbanization drive increasing stream temperatures and that their combined effects are larger than their individual effects occurring alone. Furthermore, we identify potential mitigation measures useful for waterway managers and policy makers. The results show that both local and global solutions are needed to reduce future increases to stream temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

A better understanding of assembly processes of multitrophic microbial communities and soil-borne fungal pathogens is important to predict changes in ecosystem functions. However, the drivers governing the microbial community assembly processes and soil-borne fungal pathogen distributions, particularly in the coastal ecosystem is unclear. Here, we examined the shifts in soil bacterial, fungal, protistan communities and soil-borne fungal pathogens across 2000 km in the coastal areas of eastern Australia. Determinism predominated the multitrophic microbial community assembly with mean annual temperature (MAT) differentially mediates deterministic and stochastic processes. Contrasting to protistan community, bacterial and fungal community assembly tended to be more deterministic in higher MAT regions. A total of 609 soil-borne fungal pathogens were identified with the total relative abundances ranged from 0.34 % to 52.29 % in a given soil sample. The profile of soil-borne fungal pathogens was significantly correlated with bacterial and protistan communities. Random forest indicated that MAT was the main driver of soil-borne fungal pathogens. Altogether, our work provided evidence that MAT is a critical factor driving the belowground microbial community assembly and soil-borne fungal pathogen patterns and highlighted the potential importance of trophic interactions in predicting dynamics of soil-borne fungal pathogens under the changing environment. [Display omitted] • Determinism predominated the microbial community assembly in coastal soils. • Niche overlap for bacteria, fungi and protists increased in High MAT regions. • MAT was the best predictor of soil-borne pathogens in coastal ecosystems. [ABSTRACT FROM AUTHOR]

Predicting future land change is crucial in anticipating societal and environmental impacts and informing responses at different scales. We designed an integrated, high-resolution, land-change model and forecasted Australia's land change for the years 2020, 2025 and 2030 for Cropland, Forest, Grassland, and Built-up land-uses using cloud-based and high-performance computing. A spatially explicit set of drivers was fed into a random forest classifier to generate 30-m per-class suitability layers for the country, which were then used for allocating land-use. The model was validated against 2015 data, then land-use was projected until 2030. Accuracy at the national level was ∼94%. Forecasts showed increases in Grassland and Built-up areas and decreases in Forest and Cropland. Our modelling framework expands the current capabilities of large-scale land-change models and provides a first-of-its-kind multiclass land forecast for Australia that can inform land policy at multiple scales in Australia. • We developed a continental-scale multiclass land-change model at 30-m resolution. • Land change for Australia was forecast for 2020, 2025 and 2030. • Overall accuracy at national level was 93.8% and not lower than 86% at State level. • Grassland and Built-up areas increased, while Forest and Cropland areas decreased. • The outputs provide new insights into future Australian land trajectories. [ABSTRACT FROM AUTHOR]

• The accuracy of machine learning algorithms to predict injury locations were evaluated. • 16,878 construction accident records were analyzed. • Significant contributing factors were identified using permutation importance method. The construction industry is a hazardous industry with significant injuries and fatalities. Few studies have used data-driven analysis to investigate injuries due to construction accidents. This study aims to deploy machine learning (ML) models to predict four injury types (ITs): Upper limbs, lower limbs, head/neck, and back/trunk. A total of 16,878 construction accident records in Australia were collected and fed into several ML algorithms, including fine trees, ensemble of boosted trees, xgboost, random forest, two types of support vector machines, and logistic regression. Six performance metrics of precision, recall, accuracy, F1 score, the area under the receiver operating curve (AUROC), and the area under precision recall curve (AUPRC) were used to evaluate modeling outputs. Random forest showed superior performance in predicting injury types (accuracy 79.3%; recall 78.0%; F1 score 78.5%; precision 77.1%; AUROC 0.98; and AUPRC 0.78). The critical features of injury types were analyzed using the feature importance method and accident nature and mechanism had significant impacts. The study's findings contribute to safety enhancement by providing quantitative prediction models of injury types and subsequent development of safety controls in construction. [ABSTRACT FROM AUTHOR]

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (A ustralia-wide M achine L earning ET for T rees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R2 of 0.94 (RMSE 6.63 mm period−1) and 0.92 (RMSE 6.89 mm period−1), respectively. In comparison, correlation between in-situ ET and a national ET product returned R2 of 0.44 (RMSE 34.08 mm period−1) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function. [Display omitted] • Data to quantify riverine tree evapotranspiration is underpinned by high replication of in-situ sap flow measurements. • Evapotranspiration from various field environments is reproduced using in-situ data and machine learning - R2 of 0.94 • Spatiotemporal data created to report long-term woody tree evapotranspiration (from 2012) in monthly intervals. • Random Forest model includes environmental parameters to estimate evapotranspiration across a variety of environments. [ABSTRACT FROM AUTHOR]

The warming trend over recent decades has already contributed to the increased prevalence of heat-vulnerable chronic diseases in many regions of the world. However, understanding the relationship between heat-vulnerable chronic diseases and heatwaves remains incomplete due to the complexity of such a relationship mingling with human society, urban and natural environments. Our study extends the Social Ecological Theory by constructing a tri-environmental conceptual framework (i.e., across social, built, and natural environments) and contributes to the first nationwide study of the relationship between heat-vulnerable chronic diseases and heatwaves in Australia. We utilize the random forest regression model to explore the importance of heatwaves and 48 tri-environmental variables that contribute to the prevalence of six types of heat-vulnerable diseases. We further apply the local interpretable model-agnostic explanations and the accumulated local effects analysis to interpret how the heat-disease nexus is mediated through tri-environments and varied across urban and rural space. The overall effect of heatwaves on diseases varies across disease types and geographical contexts (latitudes; inland versus coast). The local heat-disease nexus follows a J-shape function—becoming sharply positive after a certain threshold of heatwaves—reflecting that people with the onset of different diseases have various sensitivity and tolerance to heatwaves. However, such effects are relatively marginal compared to tri-environmental variables. We propose a number of policy implications on reducing urban-rural disparity in healthcare access and service distribution, delineating areas, and identifying the variations of sensitivity to heatwaves across urban/rural space and disease types. Our conceptual framework can be further applied to examine the relationship between other environmental problems and health outcomes. • This study constructs a tri-environmental framework to examine heat-disease relationship. • It contributes the first nationwide fine-grained study on heat-disease relationship in Australia. • It evaluates importance of heatwaves and 48 tri-environmental variables on six chronic diseases. • It finds that people with onset of diseases have various sensitivity and tolerance to heatwaves. • The effect of heatwaves on diseases is marginal compared to that of tri-environmental variables. [ABSTRACT FROM AUTHOR]

[Display omitted] • Currently no continuous map of ESP across the Murray-Darling Basin, Australia. • Random Forest model incorporating range of covariates to predict ESP to 1 m depth. • ESP increased with depth and was higher in the western portion of the region. • Model fit moderate, but parsimonious. • Known prediction accuracy and precision following evaluation. The Murray-Darling Basin is a highly significant agricultural region in Australia, contributing AUD $24 billion to the Australian economy each year through food and fibre production. While the region provides favourable conditions for agriculture, it faces a multitude of challenges, including climate change, drought, and soil degradation. The occurrence of excess sodium ions in the soil, referred to as 'sodicity', is a form of soil degradation which is also a significant constraint to production. Maps of sodicity exist in the Murray-Darling Basin, but only cover portions of the region and most are only measures of surface Exchangeable Sodium Percentage (ESP), rather than at depth. Therefore, the current study sought to produce 3D maps of ESP at 10 cm depth increments to 100 cm, and maps of depth to ESP constraints across the Murray-Darling Basin. Random Forests served as the model algorithm, and our results provide known measures of accuracy and precision for a transparent and reliably assessed product. We aimed to produce a more parsimonious model, avoiding the inclusion of a superfluous number of covariates, through a simplified recursive feature elimination procedure, which resulted in a reduction of 20 to 9 covariates for modelling. Following model tuning, independent testing showed the 3D model was of a moderate quality (LCCC = 0.66, r2 = 0.49, RMSE = 5.99 %, Bias = 0.14 %). Spatial cross-validation demonstrated that the model did not predict so well at under-represented locations (LCCC = 0.45, r2 = 0.29, RMSE = 7.19 %, Bias = 0.77), producing a substantially lower fit compared to the independent test fit. Area of Applicability analysis supported this notion, identifying that the model was only applicable to small pockets across the region and suggesting we were extrapolating too far beyond the feature space of the training points. Although there is room for improvement, providing transparency via measures of accuracy, precision, and applicability, provides end users with a starting point for further analyses and exploration. [ABSTRACT FROM AUTHOR]

Conventional housing price prediction methods rarely consider the spatiotemporal non-stationary problem in a large data volumes. In this study, four machine learning (ML) models are used to explore the impacts of various features – i.e., property attributes and neighborhood quality - on housing price variations at different geographical scales. Using a 32-year (1984–2016) housing price dataset of Metropolitan Adelaide, Australia, this research relies on 428,000 sale transaction records and 38 explanatory variables. It is shown that non-linear tree-based models, such as Decision Tree, have perform better than linear models. In addition, ensemble machine learning techniques, such as Gradient-Boosting and Random Forest, are better at predicting future housing prices. A spatiotemporal lag (ST-lag) variable was added to improve the prediction accuracy of the models. The study demonstrates that ST-lag (or similar spatio-temporal indicator) can be a useful moderator of spatio-temporal effects in ML applications. This paper will serve as a catalyst for future research into the dynamics of the Australian property market, utilizing the benefits of cutting-edge technologies to develop models for business and property valuation at various geographical levels. • Spatio-temporal non-stationary is significant in explanation of the variations in housing price; • A proposed spatiotemporal lag is incorporated to increase the accuracy of the models; • Non-linear tree-based models have better performance than the linear model. • Ensemble ML techniques are powerful methods to produce better predictive performance. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (T min , T max and T avg oC), rainfall (R n mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the T avg and R n inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction. [Display omitted] • Bay sediment lead (Pb) is predicted using integrative artificial intelligence model. • AI models are consisted of feature selection algorithms and prediction models. • In-situ and meteorological parameters are used for the modeling development. • Results gave an informative insight on the essential parameters influencing the Pb. • The research confirmed the feasibility of integrative AI models for Pb prediction. [ABSTRACT FROM AUTHOR]

• Soil coarse fragment data tend to be unreliable, and difficult to model and map. • Survey and agronomic observations increased the dataset × 10 (n = 39,880) • Predicted 3D coarse fragment layers outperformed other maps and were plausible. • New predictions achieved overall accuracies 0.74 to 0.92 and AUC 0.79 to 0.89. • Mapping soil features is a useful compliment to continuous property prediction. The spatial distribution of soil coarse fragments (CF) is important for a variety of agricultural and environmental applications because it directly impacts soil processes including hydrology and nutrient cycling. However, there is often insufficient measured data to reliably model and map CF using a digital soil mapping approach. By targeting CF layer occurrence rather than CF as a continuous soil property, we increased the number of data sites available for spatial modelling which improved predicted patterns. We define CF layers as hard CF and segregations size > 2 mm, > 20% by volume, and > 10 cm thick, the definition of a ferric diagnostic horizon in the Australian Soil Classification system. Highly variable legacy data yielded nearly 40,000 georeferenced sites over the 1 M square kilometre study area. The binary classification models evaluated were random forest using regression trees (probability machines) and classification trees, with and without class balancing. The best performing models were regression forests, followed by classification using the threshold that maximised the Kappa coefficient. Prediction accuracy was determined by validating with a subset of legacy data randomly selected on an unaligned grid and withheld from model training; validation was performed on all modelling methods, plus other available CF digital soil maps. Incorporating low quality legacy observations improved CF predictions significantly. The final maps depict CF layer presence or absence in four depth slices (0–5, 5–15, 15–30, and 30–80 cm) and anywhere within the top 80 cm, for both CF of any composition and specifically for ironstone gravel (sesquioxide nodules). These new maps have high predictive power: ironstone gravel layers had AUC ranging from 0.86 to 0.89, Kappa between 0.48 and 0.52, and overall accuracy from 0.82 to 0.92; for CF layers of mixed composition AUC ranged from 0.79 to 0.84, with Kappa 0.43 to 0.46, and overall accuracy from 0.74 to 0.88. The maps are plausible representations of local variation in soil properties across the landscape. They reflect the expert knowledge encoded in conventional soil maps, and are more locally credible than other available modelled CF maps. Modelling CF as layers rather than continuous properties led to high accuracy spatial representation of simple but still useful soil features for our study area. These soil feature maps complement the quantitative soil property surfaces common to digital soil mapping studies that are frequently constrained by data availability. [ABSTRACT FROM AUTHOR]

The source of sedimentary organic matter in lakes can help to elucidate climate and catchment variation and processes that reflect lake development. Common techniques for tracing sediment organic matter sources, such as the stable isotopes and elemental concentrations of C and N, can be too imprecise to identify the specific provenance of organic matter. By contrast, organic geochemical techniques such as gas or liquid chromatography and nuclear magnetic resonance provide detailed organic molecular characterisation but are both expensive and time consuming. Fourier Transform Infrared (FTIR) spectroscopy is a rapid, non-destructive, and well-established method for determining the constituents of lake sediments. However, the potential for identifying the sources of organic matter in lake sediments has not been fully explored. In this study, we assess the extent to which FTIR can be used to identify varying organic matter sources through analysis of modern autotrophs from Blue Lake, North Stradbroke Island, Australia. We investigated spectral processing techniques to identify the approach that could most accurately classify autotroph samples. Three autotroph groups (i.e algae, aquatic macrophytes, and terrestrial) were correctly classified 90% of the time. Processed spectra then became the basis of a model that used multivariate random forests to estimate sediment organic matter composition source from a sediment record from Blue Lake that spans the last 7500 years. FTIR-based estimates suggested that throughout the history of the lake, algae contributed the highest amount of organic matter to the sediment samples. These results allow a refinement of a previous study of C:N and δ13C from the same core and suggests that alterations in C:N and, particularly, δ13C reflect chemical changes in algae through time. This study demonstrates that FTIR spectroscopy is a promising tool to elucidate sources of sediment organic matter in lake sediments. • First attempt to predict sediment organic matter origin using FTIR spectroscopy. • Classification success of autotroph samples was 90%. • A random forest model was used to estimate sediment organic matter origin. • Model estimates more accurate with one source dominant, less accurate with multiple sources. • Sediment estimates indicate algal dominance throughout Blue Lake history. [ABSTRACT FROM AUTHOR]

This study describes the supervised support vector regression method and cuckoo optimization algorithm (COA-SVR), a newly developed mineral potential modelling (MPM) technique, and a case study of its application to predicting gold potential in the Granites-Tanami Orogen (GTO), Australia. COA-SVR, which was borne out of a computer program coded in MATLAB®, incorporates a popular radial basis function (RBF) known as the Gaussian kernel function. The COA-SVR model was trained using a series of predictor maps previously generated and described by Roshanravan et al. (2020a), and corroborated by way of a 10-fold cross-validation. The modelling results indicate that the COA-SVR approach to MPM outperformed the previous data-driven (i.e., random forest) and continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) models of Roshanravan et al. (2020a). As such, the newly proposed COA-SVR method presents a valid approach to MPM. To further constrain the results, the gold prospective domains identified via COA-SVR and the previous models of Roshanravan et al. (2020a) were delimited, using the student's t-value and concentration-area (C-A) fractal techniques. Following the above, indices of overall performance (O e), accuracy (A), kappa (K) and normalized density (N d) were applied to further assess the gold prospective domains as delimited by the student's t-value and C-A fractal techniques. The results are consistent with the COA-SVR and random forest models having outperformed the more traditional modelling approaches documented by Roshanravan et al. (2020a). Given the positive results obtained in this study and similarly positive results reported by others, we believe that the apparent superior nature of machine-learning algorithms strongly merits further consideration, research and more widespread application in MPM. [Display omitted] • New approach to mineral potential modelling called a COA-SVR • Approach was tested on orogenic gold systems in the GTO, Australia. • Results were compared to previously generated mineral potential models. • Results indicate that the COA-SVR method presents a valid approach to MPM. • Study highlighted there is a direct correlation between the indices of Oe , A and K. [ABSTRACT FROM AUTHOR]