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Groundwater pollution susceptibility mapping using parsimonious approaches with limited data is of utmost importance for water resource and health planning, especially in data-scarce regions. Current research assesses groundwater nitrate susceptibility by considering the various combination of explanatory variables. In this study, the novel machine learning models of weighted subspace random forest (WSRF) and generalized additive model using LOESS (GAMLOESS) are applied, and the results are compared with well-known machine learning models of K-nearest neighbors (KKNN) and random forest (RF). The optimum combination of inputs for groundwater nitrate susceptibility mapping is identified using the k-fold cross-validation methodology. Results indicated that the combination of variables of precipitation, groundwater level, and lithology had the best performance among the 16 combinations. Modeling performance using the optimum combination demonstrated that the new ensemble approach, the WSRF model, had superior performance according to the evaluation metrics of accuracy (0.87), kappa (0.73), precision (0.92), false alarm ratio (0.08), and critical success index (0.75). The susceptibility assessment results of this paper can be a useful tool in developing strategies for the prevention and protection of groundwater pollution., (© 2022 National Ground Water Association.)

Aquifer storage and recovery (ASR) is considered as an innovative method and an alternative one for sustainable management of water resources that has, in recent years, attracted the attention of experts and thinkers. Implementation of this method would entail the participation and collective action of various stakeholders. In this process, farmers are considered as the most important stakeholders; and limited studies have been conducted on their intentions to participate in collective actions of ASR management. In this regard, the investigation of farmers' intention to participate in ASR and its determinants, using social identity models of collective action, was selected as the main purpose of the present study. For this purpose, using a cross-sectional survey, 330 Iranian farmers were interviewed. In this study, the ability of the dual-pathway model of collective action (DPMCA) and the encapsulation model of social identity in collective action (EMSICA) was evaluated and compared to explain farmers' intentions towards participation in ASR management. The results revealed that the both models had good predictive powers. However, DPMCA was a stronger framework than EMSICA for facilitating farmers' collective behaviors in the field of participation in ASR management. This is one of the most important results of the present research that might be used by various users including decision makers, managers, and practitioners of water resources management in Iran and generally the world. Finally, the creation of a " we thinking system " or social identity in the field of ASR management was highlighted as one of the most important take-home messages., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Valizadeh, Bagheri-Gavkosh, Bijani and Hayati.)

This study presents a comprehensive review of the Land subsidence (LS) cases, as a worldwide environmental, geological, and global geohazard concern. Here, 290 case studies around the world mostly conducted in large metropolitan cities (e.g. Bangkok, Beijing, California, Houston, Mexico City, Shanghai, Jakarta, and Tokyo) in 41 countries were collected. The spatial distribution of LS characteristics (e.g. intensity, magnitude, and affected area), impacts, and influential factors are scrutinized. Worldwide attempts to remedy the crisis of LS were also investigated in this review. It is shown that the coastal plains and river deltaic regions are of high-frequent subsided areas around the world (~47% of 290 study areas). The spaceborne monitoring of LS is the more prevalent technique (~ 38% of total cases) compared to the ground-investigation (e.g. geological surveying, leveling, GPS, and modeling). Human-induced LS cases are 76.92% of all the LS cases around the world and groundwater extraction contributes 59.75% of these cases. Strong direct correlations with the exponential trend are observed between the average LS rate (LS avg ) with groundwater withdrawal (R 2  = 0.950) and groundwater level decline (R 2  = 0.888). To understand the influential factors on LS occurrences, the relationship of LS rate with climate factors, hydrogeological characteristics of the aquifer, human-induced factors are investigated. Finally, we provide future research guidelines and implications that need to be expanded in order to better monitor and reduce the impact of the LS phenomenon. The outcomes of this study can be used to derive a framework helpful for interpreting the observed LS phenomena and for forecasting future situations to mitigate or control this geohazard., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)