Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh

Abstract

Accurate prediction of groundwater levels is a key concern for environmental monitoring and sustainable water resources management. Inspired by a Random Forest (RF) model trained with three popular hyperparameter optimizations—Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA)—this research proposes a new method for flattening groundwater level forecasting. The study dataset includes groundwater table (GWT) information and two independent variables, latitude and longitude, collected as a single-time measurement from 335 observation wells in Bangladesh’s Bogura district. The performance of models was measured using some metric form, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2, and Mean Absolute Error (MAE). The PSO-based optimization (PSO-RF) was more effective than GA and SA, observing the prediction ability, as reported in the study. The PSO-RF model demonstrated better generalization on the test data by achieving a Test MSE of 0.42561, a Test RMSE of 0.65239, a Test R² of 0.84093, and a Test MAE of 0.49997, which were lower than those from all used models. This paper demonstrates the significance of hyperparameter optimization in enhancing the performance of machine learning models for environmental predictions. 2D and 3D spatial GWT distribution maps were carried out with the derived data set, consequently established by ArcGIS software. The proposed PSO-RF model is promising for the prediction of groundwater levels in Bangladesh. It can be further extended to other regions for similar applications in environmental problem prediction and water resource management