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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 assess the goodness-of-fit and predictive performance aspects of the models. Validation results indicated that the conditional mixed MRF (with AUC = 0.805, TSS = 0.692, MCC= 0.692, F-score= 0.846, E = 0.846, MR = 0.153) outperformed the other models when producing groundwater nitrate pollution maps. The next best model was RF. All models identified hydraulic conductivity(with &gt; 50%) as the most important variable for investigating groundwater nitrate pollution, whereas lineament density (with &lt;7%) was the least important. Since the goodness-of-fit of all applied models ranged from very good (0.7 &lt; AUC &lt; 0.8) to excellent (0.9 &lt; AUC &lt; 1) based on AUC, SO this study revealed a promising method to accurately map groundwater nitrate pollution in support of more effective water quality management.

A novel artificial intelligence-based approach for mapping groundwater nitrate pollution in the Andimeshk-Dezful plain, Iran