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• 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]

• 8 field sampled soil organic carbon datasets were compared with topography. • Attributes were derived from a 5 m, 25 m, 30 m and 90 m digital elevation model. • A random forest model was applied across three spatial scales. • Spatial scale affected model performance and the variables selected to predict SOC. • Local scale soil organic carbon prediction needs high resolution data. Baseline estimations of soil organic carbon (SOC) have been made globally using SOC models, earth system models, and digital soil mapping techniques. Digital elevation models (DEMs) underpin these analyses to incorporate the effect of topography on SOC. Here, we test the effect of DEM resolution on the relationships between topography and SOC across catchment to hillslope scales. Samples were collected using a nested field sampling approach in a grazed catchment on Eastern Australia. Topographic attributes were derived from 5 m, 25 m, 30 m, and 90 m resolution DEMs and then used to predict SOC using a Random Forest model. This was trained on a catchment-wide dataset and tested using the repeat samples of this dataset and finer scale field data. SOC was able to be predicted using topography in the catchment-wide datasets (model R2 = 0.37–0.51), however not in the finer-scale data (model R2 = 0–0.21). Variable importance was calculated from the modelling process, with elevation (as a surrogate for climate) being the main driver at the catchment-scale. In the finer scale datasets, topographic variables linked to soil redistribution were more important. As a result, SOC estimation methods using coarse resolution DEM data and large-scale sampling may be limited in capturing the effect of topography, having implications for SOC management and modelling. [ABSTRACT FROM AUTHOR]

Predictive mapping of seabed sediments based on multibeam bathymetric (BM), and backscatter (BS) data is effective for mapping the spatial distribution of the substrate. A robust modeling technique, the random forest decision tree (RFDT), was used to predict the seabed sediments in an area of the Joseph Bonaparte Gulf, Northern Australia, using the multibeam data and seabed sediment samples collected simultaneously. The results showed that: (1) Using multibeam bathymetry data in addition to multibeam backscatter data improves the prediction performance of the RFDT. In comparison to only multibeam backscatter data, the prediction performance achieved a ~10% improvement in sediment properties; it achieved a ~44.45% improvement of overall accuracy in sediment types, and a ~0.55 improvement in Kappa. (2) The underlying relationships between sediment properties and multibeam data show that there is an opposite non-linear correlation between sediment property-BS and sediment property-BM. For example, there is an obvious negative relationship between %mud-BS at incidence angles of 13° and 21°, but the relationship between %mud-BM is positive. As such, the RFDT is a useful and well-performing method in predicting the relationship between sediment properties and multibeam data and in predicting the distribution of sediment properties and types. However, the sediment prediction method in deep-water areas with high gravel content needs to be further evaluated. [ABSTRACT FROM AUTHOR]