Conjunct application of machine learning and game theory in groundwater quality mapping

Abstract

AbstractGroundwater quality (GWQ) monitoring is one of the best environmental objectives due to recent droughts and urban and rural development. Therefore, this study aimed to map GWQ in the central plateau of Iran by validating machine learning algorithms (MLAs) using game theory (GT). On this basis, chemical parameters related to water quality, including K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, pH, TDS, and EC, were interpolated at 39 sampling sites. Then, the random forest (RF), support vector machine (SVM), Naive Bayes, and K-nearest neighbors (KNN) algorithms were used in the Python programming language, and the map was plotted concerning GWQ. Borda scoring was used to validate the MLAs, and 39 sample points were prioritized. Based on the results, among the ML algorithms, the RF algorithm with error statistics MAE = 0.261, MSE = 0.111, RMSE = 0.333, and AUC = 0.930 was selected as the most optimal algorithm. Based on the GWQ map created with the RF algorithm, 42.71% of the studied area was in poor condition. The proportion of this region in the classes with moderate and high GWQ was 18.93% and 38.36%, respectively. The results related to the prioritization of sampling sites with the GT algorithm showed a great similarity between the results of this algorithm and the RF model. In addition, the analysis of the chemical condition of critical and non-critical points based on the results of RF and GT showed that the chemical aspects, carbonate balance, and salinity at critical points were in poor condition. In general, it can be said that the simultaneous use of MLA and GT provides a good basis for constructing the GWQ map in the central plateau of Iran.