LAUSR

Abstract Groundwater crisis across the world is a thought-provoking issue and for resolving the problem, it is highly necessary to identify the potential groundwater zones and estimate water yield. The present work intends to identify potential groundwater zone based on ensemble modeling assembling advance machine learning algorithm like Random Forest (RF), Radial Basis Function (RBFnn) and Artificial Neural Network (ANN) and set theories like union and intersection based modeling using 15proxyconditioning parameters for developing sustainable water resource management plan. The work is based on Tangon river basin of Barind tract of Eastern Indian and Bangladesh, suffers from water scarcity. Groundwater potentiality models have identified 34.93–35.67% area to total basin area (2388.88 km2) at the proximity of lower reach of the main river as very high to highly potential for groundwater. Among the employed parameters elevation, slope, land use/land cover, distance from perennial segment of stream are identified as the dominant in this case. For assessing the accuracy level of the models, Receiver operating characteristics (RoC), proximity test and aquifer breadth data are used. Both the ensemble models and set theory centric models show very good to excellent performances suitably identifying groundwater potentiality. However, the performance level of ensemble modeling is more satisfactory. As the groundwater potentiality zones of the study area are well delimitated, this study may be useful for adopting a suitable water resource management plan.