Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

Abstract

Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment.