Simulation of nitrate contamination in groundwater using artificial neural networks

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017-2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.

Application of machine learning in groundwater quality modeling - A comprehensive review

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.

Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling

Agricultural activities have become a major source of groundwater nitrate contamination. In this context, this study aims to analyse nitrate concentrations in a shallow aquifer of Mahdia-Kssour Essef in central-eastern Tunisia, identify the assignable sources, and predict the future levels using artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models. The results showed that nitrate concentrations measured in 21 pumping wells across the plain ranged from 17 to 521 mg L^-1. A total of 67% of the monitoring points greatly exceed the standard guideline value of 50 mg L^-1. The main relevant anthropogenic and natural factors, such as soil texture, land use, fertilizers application rates, livestock waste disposal, and groundwater table, are positively correlated with groundwater nitrate concentration. The ANN model showed good fitting between measured and simulated results with coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) values of 0.88, 53.95, and 39.64, respectively. The ARIMA applied on annual average nitrate concentrations from 1998 to 2017 revealed that the best fitted model (p, d, q) is (1, 2, 1). The R² value is approximately 0.36, and the Theil inequality coefficient and bias proportion values are small and close to zero. These results proved the ARIMA model's adequacy in forecasting annual average nitrate concentrations of 116 mg L^-1 in 2025. These findings may be useful in making groundwater management decisions, particularly in rural and semi-arid areas, and the proposed ARIMA model could be used as a managed tool to monitor and reduce the nitrate intrusion into groundwater.

Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques

The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results.

Comparative evaluation of machine learning models for groundwater quality assessment

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

Contribution of nitrate-nitrogen concentration in groundwater to stream water in an agricultural head watershed

This study characterized nitrate-nitrogen (NO₃-N) concentrations in groundwater and stream water in an agricultural head watershed in South Korea and identified the pollution load of NO₃-N as a result of the groundwater entering streams using field surveys, analyses of chemical constituents, and numerical modeling. The mean NO₃-N concentration in groundwater was 7.373 mg/L, which is approximately 1.9 times higher than concentrations found in stream water. The groundwater and stream water samples belonged to the Ca-HCO₃ type. The concentration of NO₃-N in groundwater tended to increase in the lowland areas downstream. There was seasonal variations of NO₃-N in both the groundwater and stream water samples, with increases in concentration during the dry season (January-April) and decreases during the wet season (June-October). The NO₃-N load in stream water to that in groundwater (R) was higher during the wet season (September) than the dry season (March), with R distinctly increasing in upstream areas relative to downstream areas, indicating that during the wet season, a large amount of NO₃-N is introduced into stream water from groundwater. By analyzing the relationship between groundwater and stream water and through NO₃-N transport modeling, it was revealed that in the watershed, the nitrate-N load in stream water is greatly augmented by inputs from groundwater, particularly in the middle and downstream areas.