How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?

Abstract

The prediction of groundwater levels in arid and semi-arid regions is of great importance to tailor the best water management strategies. In this study, we propose a new approach that combines simulation, clustering, and optimization tools for groundwater level prediction. This approach simulates groundwater levels (GWL) using the MODFLOW method, clusters the study aquifer into different clusters using the k-mean method, and predicts regional GWL using the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods that were optimized by the Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). The efficacy of our approach was evaluated via a case study in northwest Iran. The MODFLOW method simulated the distribution of GWL across the study area with R2 = 0.99, root mean square error (RMSE) = 0.97 m (m) and mean absolute error (MAE) = 0.82 m. The k-means method clustered the aquifer into seven clusters based on the hydraulic conductivity, storage coefficient, groundwater level, groundwater depth, groundwater withdrawal, and aquifer saturation thickness parameters. The prediction of groundwater level for each cluster demonstrated the accurate performance of all optimized models with mean RMSE = 0.6 m and mean absolute percentage error (MAPE) = 0.23 m. The prediction phase identified groundwater level in the previous month (obtained from the MODFLOW method), withdrawal of aquifer, precipitation, temperature, and evaporation as the most influential variables for groundwater levels in different clusters. We recommend the methodology proposed here for the prediction of groundwater levels in different aquifers with heterogeneous characteristics that pose computational burdens and uncertainties.