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Flood mitigation behavior is essential for effective flood risk management, particularly in Australia, where the federal government has increasingly emphasized individual responsibility for preventing and preparing for flood disasters. Despite this emphasis, around 60% of flood-prone residents hesitate to adopt private mitigation measures. Their reluctance highlights the complexities of decision making regarding the implementation mitigation measures, emphasizing the need for a comprehensive understanding of flood mitigation behavior among Australian residents. In order to address this knowledge gap, we conducted household surveys in flood-prone regions of Australia and used six machine learning algorithms—logistic regression, k -nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network—to analyze the proposed framework for flood mitigation behavior. The results of five-fold cross-validation demonstrated that the random forest algorithm provided superior accuracy for predicting protection motivation compared to the other five algorithms. Furthermore, seven of the 37 predictors utilized in the model had a higher impact on protection motivation than the other predictors. These influential predictors included self-efficacy, response efficacy, response costs, key variables within the coping appraisal factor, fear or worry about future flood risk, one variable associated with the threat appraisal factor, past experiences of flooding inside the house linked to the prior flood experience factor, and the implementation of flood mitigation measures by individuals connected to the householder, such as family, friends, and neighbors, which pertains to the social environment factor within the proposed framework for flood mitigation behavior. These predictors were deemed adequate to be used as input variables in the random forest model, because they provided accuracy and performance similar to when all 37 predictors were included. These insights into influential predictors can be used to develop an optimal prediction model for protection motivation in flood-prone areas of Australia and reduce the effort needed to collect the required data during postdisaster surveys. This research is of value to policymakers and floodplain managers in designing effective flood mitigation strategies and promoting the adoption of protective measures in flood-prone communities to reduce flood risk. [ABSTRACT FROM AUTHOR]

The marine biogeochemical time-series products, which include total alkalinity, inorganic carbon, nitrate, phosphate, silicate, and pH, constitute a foundational support mechanism for the ongoing surveillance of oceanic biogeochemical changes. These products play a critical role in facilitating research focused on dynamic monitoring of marine ecosystems and fostering sustainable oceanic development. However, existing monitoring methodologies are hampered by inherent limitations, notably the paucity of observational products that simultaneously offer high spatial and temporal resolutions. Furthermore, the interpolation methods typically employed in these contexts frequently prove low-effective on a large scale, resulting in data with extensive temporal and spatial expanses that are difficulty for applications aimed at monitoring large-scale ocean dynamics. A novel integration of the CANYON-B and Random Forest regression methods was explored to address these challenges in reconstructing key marine biogeochemical parameters. This work reconstructs the concentrations of these marine biogeochemicals at the sea surface within Australia's Exclusive Economic Zone over the period from 2000 to 2022 on a 1-kilometre scale. The approach involves the amalgamation of multi-source in-situ ocean chemistry time-series observations with MODIS Terra ocean reflectance imagery and ocean water colour product distributions. This research highlights the substantial capabilities of machine learning for the large-scale reconstruction of ocean chemistry data, introducing a new, viable method for utilising in-situ measurements and optical imagery in reconstructing marine biogeochemical elements, thereby significantly enhancing our ability to monitor large-scale ocean dynamics. The datasets generated and analysed in this study are available on Science Data Bank (https://doi.org/10.57760/sciencedb.09331) (Zhang et al., 2024) [ABSTRACT FROM AUTHOR]

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000–2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000–2019), and year-on-year results were highly correlated (94–97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting. [ABSTRACT FROM AUTHOR]

Assessment of the spatial distribution of potential pathways of sediment transport and the degree of linkage between sediment sources and the channel network within a watershed represents a valuable analysis for informing management decisions on sediment yield and transfer. Given the limitations of conventional methods for determining index of sediment connectivity (IC), there is a need to provide a flexible and efficient approach with the ability to apply different factors. In this regard, five decision tree-based machine learning models: M5 prime (M5P), random tree (RT), random forest (RF), alternating model tree (AMT), and reduced error pruning tree (REPT) were tested using geomorphic and climatic factors. Two databases were constructed with 200 and 1600 classes at 50 watersheds in Queensland, Australia. In these models, IC was assessed as an output parameter and six attributes that affect IC were assigned as input parameters (i.e., elevation, slope, area, length of stream channel, normalized difference vegetation index, and rainfall). Statistical validation and comparison of model predictions with calculated IC values based on the approach of Borselli et al. (Catena 75:268–277, 2008) were performed. Based on the statistical criteria, the RF model produced the most robust estimations of IC compared to other models and performed very well for IC modelling, especially in smaller subsections of watersheds. Accordingly, these findings can play an effective role for implementing watershed management and soil and water resources management measures. [ABSTRACT FROM AUTHOR]

Spatially explicit prediction of soil organic carbon (SOC) serves as a crucial foundation for effective land management strategies aimed at mitigating soil degradation and assessing carbon sequestration potential. Here, using more than 1000 in-situ observations, we trained two machine learning models (random forest, and K-means coupled with multiple linear regression), and one process-based model (the vertically resolved MIcrobial-MIneral Carbon Stabilization (MIMICS)) to predict SOC content of the top 30 cm of soil in Australia. Parameters of MIMICS were optimized for different site groupings, using two distinct approaches, plant functional types (MIMICS-PFT), and the most influential environmental factors (MIMICS-ENV). We found that at the continental scale, soil bulk density and mean annual temperature are the dominant controls of SOC variation, and that dominant controls vary for different vegetation types. All models showed good performance in SOC predictions with R2 greater than 0.8 during out-of-sample validation with random forest being the most accurate, and SOC in forests is more predictable than that in non-forest soils. Parameter optimization approaches made a notable difference in the performance of MIMICS SOC prediction with MIMICS-ENV performing better than MIMICS-PFT especially in non-forest soils. Digital maps of terrestrial SOC stocks generated using all the models showed similar spatial distribution with higher values in southeast and southwest Australia, but the magnitude of estimated SOC stocks varied. The mean ensemble estimate of SOC stocks was 30.08 t/ha with K-means coupled with multiple linear regression generating the highest estimate (mean SOC stocks at 38.15 t/ha) and MIMICS-PFT generating the lowest estimate (mean SOC stocks at 24.29 t/ha). We suggest that enhancing process-based models to incorporate newly identified drivers that significantly influence SOC variations in different environments could be key to reducing the discrepancies in these estimates. Our findings underscore the considerable uncertainty in SOC estimates derived from different modelling approaches and emphasize the importance of rigorous out-of-sample validation before applying any one approach in Australia. [ABSTRACT FROM AUTHOR]

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]

There is increasing interest in the phenotypic plasticity of tree species to variation in climate as it may affect the economic value of current and future plantations. This study applied ensemble learning methods to explore the plastic response of Eucalyptus nitens pulpwood selection traits of growth (DBH, diameter at 1.3 m height), wood density and Kraft pulp yield to variation in elevation, geography and associated climate variables. To help explain the pulp yield response, we also modelled underlying biological traits—cellulose, lignin and extractives. The study was based on data from 84 harvest-age plots of common genetic origin across the pulpwood plantation estate in north-western Tasmania, Australia. DBH and wood density were obtained from resistance profile traces, collected on standing trees using a drilling resistance tool. In addition, outerwood cores were taken at 1.3 m stem height for (i) calibrating the resistance measures for wood density, and (ii) for assessment of pulp yield and wood chemistry based on near infrared spectroscopy. Modelling of the variation in plot means using the random forest algorithms showed growth and wood properties were influenced by the growing period climate of the plot. Of the climate variables studied, growth was mainly influenced by temperature, while wood density was mainly affected by rainfall-related variables. Wood density varied independently of growth and decreased with increasing annual rainfall and elevation of the plots. Pulp yield had the poorest fit statistics, it was influenced by a mix of climatic and geographic variables, and appeared independent of variation in growth and wood density. The plot variation in pulp yield was best explained by the modelled trends in the underlying biological traits of cellulose and lignin. Using these models, the plastic response of the key pulpwood traits to climate was mapped across the E. nitens plantation estate to help predict current plantation attributes and guide future choices of sites for plantation establishment. [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]

Wildlife strikes in aviation represent a serious economic concern; however, in some jurisdictions, the costs associated with this phenomenon are not collected or shared. This hampers the industry's ability to quantify the risk and assess the potential benefit from investment in effective wildlife hazard management activities. This research project has applied machine learning to the problem by training a random forest algorithm on wildlife strike cost data collected in the United States and predicting the costs associated with wildlife strikes in Australia. This method estimated a mean annual figure of AUD 7.9 million in repair costs and AUD 4.8 million in other costs from 2008 to 2017. It also provided year-on-year estimates showing variability through the reporting period that was not correlated with strike report numbers. This research provides a baseline figure for the Australian aviation industry to assess and review current and future wildlife hazard management practices. It also provides a technique for other countries, airlines, or airports to estimate the cost of wildlife strikes within their jurisdictions or operational environments. [ABSTRACT FROM AUTHOR]

Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES), together with assimilated PBL heights from NASA Modern-Era Retrospective analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging Spectro-Radiometer (MISR) observations and a random forest, machine-learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability of plume injection fractions, resulting in a 63 % underestimate of daily fire emission fluxes injected above the PBL. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2 = 0.88, normalized mean bias = 10 %), which predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to the best model agreement with observed surface PM2.5 in several key cities, with smoke PM2.5 accounting for 5 % to 52 % of total PM2.5 during fire seasons from 2009 to 2020. [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]

Background: Thirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients. Method: Data used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Results: The models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658). Conclusion: Discriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up. [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]

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]

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]

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]

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 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]

Backgrounds and aims: Early symbolic play abilities are closely related to long-term language development for both autistic and non-autistic children, but few studies have explored these relations for different dimensions of pretence and of language. The current study explores carer-reported measures of solitary symbolic play, object substitution and peer role play abilities at age 3, and their respective relations with parent-reported semantics, syntax and narrative abilities at age 7 for both autistic and non-autistic children. Methods: We conducted secondary data analyses exploring links between different aspects of pretence and of language on the Longitudinal Study of Australian Children population cohort. We identified 92 autistic children and used propensity score matching to match them with 92 non-autistic children based on demographic and developmental information such as non-verbal IQ and socioeconomic status. We explored concurrent and longitudinal relations using correlation and regression models. Results: Both correlational and hierarchical regression analyses confirmed the significant effects of age 3 symbolic play abilities in facilitating age 7 semantics, syntax and narrative abilities for autistic children. We found that object substitution held most prominent influence, followed by peer role play and solitary symbolic play. In contrast, for non-autistic children, none of the age 3 symbolic play abilities were significant predictors, whereas socioeconomic status at birth and age 3 language abilities held significant influences on their age 7 semantics, syntax and narrative abilities. Conclusion: We discuss the implications of our findings for play interventions targeting language outcomes. [ABSTRACT FROM AUTHOR]

Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyperparameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, Lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, Lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants. [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]

To understand how ecological communities will respond to global change we need new tools and datasets on species across large spatial and temporal scales. Hyperspectral reflectance 'spectra' capture a promising set of traits that show potential to be scaled up in time and space via remote sensing. Thus far, spectra have been shown to distinguish the taxa and trait responses of a substantial number of species within a plethora of vascular plant communities, but not yet for biological soil crust communities (biocrusts).Here, we assess if spectra can be applied to identify biocrust species and their trait variation. We collected biocrust specimens across an aridity gradient spanning 650 km within drylands of Eastern Australia and acquired their spectra, over 12,700 spectral readings, with a high‐resolution radiospectrometer. A machine learning method (random forests) was used to assess how well the spectra of biocrust specimens could distinguish their species and broader structural and chemical traits.Spectra were able to differentiate a substantial number of biocrust species (35) with considerable accuracy (~78.5%). Furthermore, spectral features related to chemical traits were found to primarily drive species spectral differences.Synthesis. Our findings establish that biocrust species hold unique and detectable spectral responses, providing an important basis for remote sensing applications on biocrust species and their trait responses across dryland systems. [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]

Forest fire severity influences post-fire fuel structure and thus the behaviour of subsequent fires. Understanding such interactions is critical to improving predictions of fire risk and emergency management, yet few studies have quantified fire severity effects on fuel attributes. We quantify fuel structure of a fire-tolerant eucalypt forest 7 years after a landscape-scale wildfire in south-eastern Australia. We used high-density airborne lidar data to estimate understorey fuel metrics in three strata representing horizontal and vertical connectivity in 1084 plots (0.06 ha) representing four wildfire severities (unburnt, low, moderate, high). Fuel structure was changed by high-severity fire, which significantly increased the cover and horizontal connectivity of the elevated and midstorey strata and decreased space between the understorey and canopy relative to other severity types. Random Forest models indicated that understorey fuel metrics were most influenced by wildfire severity, pre-fire values of each metric, and post-fire canopy cover, and least influenced by climatic and topographic variables. Our study provides evidence of positive feedbacks to flammability by high-severity wildfire in fire-tolerant eucalypt forests through increased horizontal and vertical fuel connectivity. It demonstrates the utility of airborne lidar data for quantifying fuel structure in complex forests and providing critical data for fire risk assessments. We use airborne lidar data to examine how wildfire severity influences the horizontal and vertical connectivity of fuel in temperate eucalypt forests. Our study provides evidence of positive feedbacks to flammability and a greater potential for crown fires through increased ladder fuels after high-severity fire. [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]

· 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]

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]