Reliability of Artificial Intelligence-based Models Compared to Numerical Model for Predicting Groundwater Level under Changing Climate

Abstract

Groundwater modeling is a crucial tool for simulating groundwater level behavior under climate change scenarios, and for studying the effects of water management strategies on sustainability of groundwater resources. In this study, two types of models, namely, a physical-based numerical model called MODFLOW, and a data-driven model called Genetic Algorithm-based Multilayer Perceptron (MLP-GA), were evaluated for the reliable predictions of groundwater levels in the semi-arid region of the Karnal district, Haryana. Seven hybrid MLP-GA models were developed with different combinations of input variables such as rainfall, crop evapotranspiration, deep percolation, and irrigation water requirement. The numerical model and hybrid MLP-GA models were calibrated and validated using groundwater-level data from the pre-monsoon period. Among the hybrid models, the model M-1 with four input variables (crop evapotranspiration, rainfall, deep percolation, and applied irrigation water) and 4-29-1 (four input nodes, 29 neurons in the hidden layer, and one output node) model architecture performed the best, but the numerical model showed superiority over the MLP-GA models. The numerical model and M-1 model were used to predict future groundwater levels under projected climate change scenario. According to the numerical model, under RCP4.5 scenario, groundwater levels in the study area were projected to decline by 7.7 meters by the year 2039 compared to the reference year of 2015. The M-1 model predicted decline of 5.0 meter by the year 2039. The study concluded that all input variables are essential for accurately simulating groundwater levels using MLP-GA models, and that the numerical model is more reliable for assessing the impact of climate change on groundwater behavior for future periods.