Delineation of groundwater potential zones of upper Godavari sub-basin of India using bi-variate, MCDM and advanced machine learning algorithms

Abstract

Sustainable management of groundwater resource is a most critical due to its over exploitation and ascending stress by industrial and socio-economic factors. It is utmost important to manage this precious resource by properly identifying the suitable Groundwater Potential Zones (GPZ). Therefore, the main aim of the present study is to delineate the GPZ in the upper Godavari sub-basin of India by employing different bi-variate, Multi Criteria Decision Making (MCDM), ensembled, and Machine Learning (ML) models. These models include Weight of Evidence (WoE) (bi-variate), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (MCDM), Fuzzified Functional Ratio (F-FR) (bi-variate), Extreme Gradient Boosting (XGB) (ML) and Extremely randomized Trees (ET) ML. The ensembled model featured different combination of WoE, TOPSIS and F-FR for mapping the enhanced accuracy in predicting the GPZ. A total of 15 groundwater factors were considered where 75% of the data were selected as training data and the rest 25% as validation data. These data were used to produce the ensembled ML models. The result of the model was plotted in terms of area under curve (AUC)-Receiver Operating Characteristics (ROC) curve and selected best model. The AUC-ROC result of the obtained model was found to be WoE oE models. The result of the model was plotted in termWoE_TOPSIS = 94%, WoE_F-FR = 93%, TOPSIS_F-FR = 94% and WoE_TOPSIS_F-FR = 95%. Results clearly indicate the improved accuracy of ensembled bi-variate and MCDM model over advanced ML model. The predicted statistical properties of the ensembled model also resembled ML models and a high correlation was observed. Thus, the ensembled model can be used over advanced ML models for delineating the GPZ mapping.