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This study tested three multilayer Markov random-fields (MRFs) models – multicue MRF (MMRF), conditional mixed MRF (CMMRF), and fusion MRF (FMRF) – to produce groundwater nitrate pollution maps for the first time. Random forest (RF) was also used as a baseline model. Several cutoff-dependent and cutoff-independent evaluation metrics were used to as...
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With the impact of human engineering activities, groundwater pollution has seriously threatened the health of human life. Accurate water quality assessment is the basis of controlling groundwater pollution and improving groundwater management, especially in specific regions. A typical semi-arid city in Fuxin Province of China is taken as an example...
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... These models can reliably predict water quality, benefiting similar groundwater quality studies and confirming Al-Baha's groundwater suitability for drinking and irrigation. More recent studies of GWQ were conducted by [29,30]. The ultimate goal of this research is to bridge the gap between datadriven predictions and actionable insights in the context of groundwater quality assessment. ...
The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental and public health challenges across the world. Water quality is an essential component in sustaining environmental health. This integrated study aimed to assess indexical and spatial water quality, potential contamination sources, and health risks associated with groundwater resources in Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. The physiochemical results indicated overall groundwater pollution. This study addresses the critical issue of drinking water resource suitability assessment by introducing an innovative approach based on the pollution index of groundwater (PIG). Focusing on the eastern region of Saudi Arabia, where water resource management is of paramount importance, we employed advanced machine learning (ML) models to forecast groundwater suitability using several combinations (C1 = EC + Na + Mg + Cl, C2 = TDS + TA + HCO3 + K + Ca, and C3 = SO4 + pH + NO3 + F + Turb). Six ML models, including random forest (RF), decision trees (DT), XgBoost, CatBoost, linear regression, and support vector machines (SVM), were utilized to predict groundwater quality. These models, based on several performance criteria (MAPE, MAE, MSE, and DC), offer valuable insights into the complex relationships governing groundwater pollution with an accuracy of more than 90%. To enhance the transparency and interpretability of the ML models, we incorporated the local interpretable model-agnostic explanation method, SHapley Additive exPlanations (SHAP). SHAP allows us to interpret the prediction-making process of otherwise opaque black-box models. We believe that the integration of ML models and SHAP-based explainability offers a promising avenue for sustainable water resource management in Saudi Arabia and can serve as a model for addressing similar challenges worldwide. By bridging the gap between complex data-driven predictions and actionable insights, this study contributes to the advancement of environmental stewardship and water security in the region.
... parameters are challenging to model accurately. Additionally, limited data is often available [22,23], particularly in developing countries [24], making training and validating AI models challenging. One approach that has gained increasing attention in recent years is the use of hybrid AI models, which combine different AI techniques with traditional models or expert knowledge to improve performance and address some of the limitations of AI in groundwater management [25][26][27]. ...
Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.
... Artificial intelligence in groundwater quality studies (Chou, 2006;Han et al., 2011;Band et al., 2020;Gholami et al., 2020;Maliqi et al., 2020;Mosaffa et al., 2021) and studies on groundwater depth fluctuations (Dixon, 2004;Saemi and Ahmadi, 2008;Gong et al., 2018;Chen et al., 2020;Gholami et al., 2021) has been widely used. Further, machine learning was used in different hydrological modeling studies with a high performance (Rahmati et al., 2017;Tongal and Booij, 2018;Rahmati et al., 2019;Azizi et al., 2020;Kashani et al., 2020;Javidan and Javidan, 2021;Wells et al., 2021). Wang and ZhangDing (2017) used machine learning and a water quality index (WQI) for modeling groundwater quality in China. ...
Increased nitrate concentration is one of the main groundwater quality problems today that needs to be measured and monitored. Water quality testing and monitoring are time consuming and costly. Therefore, new modeling methods such as machine learning algorithms can be used as an efficient solution for predicting nitrate concentration. In this study, three machine learning methods including deep neural network (DNN), extreme gradient boosting (EGB), and multiple linear regression (MLR) were used to predict nitrate contamination in groundwater in the north of Iran (Mazandaran plain) and finally the best method was selected for mapping. The mean nitrate concentration in 250 piezometric wells was considered as output variable and the factors affecting groundwater quality (groundwater depth, transmissivity of aquifers, precipitation, evaporation, distance from water resources and Caspian Sea, distance from industries and residential centers, population density, topography, and exploitation from groundwater) as input variables in an alluvial aquifer. The same training and testing data were used in the modeling process of the three machine learning methods. The results of the training and testing stages showed that the EGB method has the highest performance in predicting nitrate concentration due to the lowest error values and highest correlation between the measured and predicted values of nitrate concentration (training R-sqr = 0.98, Nash–Sutcliffe efficiency (NSE) = 0.98, and test R-sqr = 0.86, NSE = 0.84). Further, the results indicate that the factors distance from industries, population density, groundwater depth, and evaporation rates are the most important factors affecting nitrate concentration in groundwater. Finally, the tested EGB model and a geographic information system (GIS) tool were used to prepare a map of groundwater nitrate pollution in the study area. Evaluating the performance of the resulting map by comparing the predicted and measured values indicated a good accuracy (R-sqr = 0.8).
