Modelling Groundwater Level Changes With Machine Learning: A Case Study of GMDH, W‐ELM and GEP in the Kermanshah Plain

ABSTRACT In this study, the simulation of groundwater fluctuations in the Kermanshah Plain is carried out via three advanced machine learning algorithms, i.e., GMDH, weighted extreme learning machine (W‐ELM) and gene expression programming (GEP). The objective of this research is to evaluate and compare the accuracy of different machine learning models in simulating and predicting such fluctuations under different conditions. The data used in this study include the monthly groundwater level, precipitation, evaporation and temperature data from 2002 to 2020. To increase the accuracy of the models, the data are processed via the min–max normalisation approach. The results obtained from the evaluation of the models using different efficiency indices, such as the root mean square error (RMSE), R 2 and NSE, indicate that the W‐ELM model, which has the best performance in both the training and testing phases, is more exact than the GMDH and GEP models. In particular, the W‐ELM achieves R 2 = 0.999, NSE = 0.979, PBIAS = 0.012 and RMSE = 0.093 in the testing phase, which indicates its high accuracy and efficiency. The results of the sensitivity analysis indicate that all the models are highly sensitive to the groundwater level lag variable GWL₋₁.This research shows that machine learning models, especially the W‐ELM model, can be utilised as effective tools for predicting groundwater level variation and the optimal management of water resources in different regions.