Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction

Vipul Bhadani, Abhilash Singh, Vaibhav Kumar, Kumar Gaurav

Abstract: Highlights•A fuzzy invasive weed optimization for GWL prediction is proposed.•The algorithm is trained and tested on large datasets (665 wells from 1997 to 2018).•GWL lag emerged as the most relevant (or important) input feature in predicting GWL.•Adding LULC data improves groundwater modeling, aiding analysis and decisions.•It outperforms seventeen benchmark algorithms (baseline, deep learning, and hybrid).