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Nitrate pollution of groundwater has increased dramatically worldwide due to increase of population and agricultural productivity. The resulting nitrate concentration in groundwater is usually a combination of various types of point and non-point pollutant sources. It is often difficult to distinguish between these sources since groundwater is formed in large and complex catchments with various natural processes and anthropogenic influence that contribute to a certain downstream nitrate concentration. For such conditions, this paper uses a methodology that can be used to inversely determine type and location of main nitrate pollutant source. The methodology builds on two state-of-the-art data mining techniques, boosted regression tree (BRT) and k-nearest neighbor (KNN). These techniques are used to produce a nitrate pollution vulnerability map. The methodology can mitigate effects of subjective judgement on determining importance of different sources and mechanisms for nitrate transport. The investigated mechanisms are hydrogeological, hydrological, anthropogenic, topography, and soil conditioning factors. Thus, the proposed methodology is used to separate between natural processes and anthropogenic effects on nitrate pollution. To calculate the groundwater vulnerability maps, a groundwater nitrate concentration of 40 mg/L (suggested by WHO with a 20% risk margin) was selected as a general threshold for identifying polluted areas that resulted in 96 polluted wells. Non-polluted locations were selected from well data with nitrate concentration less than 15 mg/L (96 non-polluted). The models were trained on 70% polluted and 70% non-polluted site data. The remaining data, 30% polluted and 30% non-polluted sites, were used to validate the simulation results. Results showed that the BRT produced outputs with higher performance than the KNN algorithm. The final ranking results based on the BRT model showed the higher importance of hydraulic conductivity, river density, soil, slope percent, net recharge, and distance from villages, in order, relative to other factors. [ABSTRACT FROM AUTHOR]

Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network. Unlabelled Image • Four tree-based models were applied for land subsidence modelling. • RF is the best model to spatially predict land subsidence hazard. • Groundwater drawdown was the most influential factor. [ABSTRACT FROM AUTHOR]

A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994–2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability. Unlabelled Image • Spatial and temporal machine learning approaches proposed for drought risk mapping. • Performance of six machine learning models is compared. • MARS and RF are suitable machine learning models for spatial risk mapping • Approximately 26% of the study area is at high or very high drought risk. • New approach considers hydro-geo-environmental factors for drought risk policy. [ABSTRACT FROM AUTHOR]