Machine Learning-Based Assessment of Long-Term Groundwater Level Dynamics in the Nakhon Luang Aquifer from 1978 to 2023

Abstract

Groundwater is a critical resource supporting economic and social development in Thailand, particularly within the lower Chao Phraya Basin, where rapid urbanization and industrial expansion have led to intensive groundwater exploitation. This study applied a random forest (RF) machine learning model to investigate groundwater level (GWL) dynamics in the Nakhon Luang aquifer, which is one of the most productive aquifers due to its high yield and relatively good water quality, using long-term data from 1978 to 2023. The seven data sets include groundwater levels, groundwater pumping, rainfall, groundwater recharge, ground surface elevation, geological information, and lag-time features incorporated to represent delayed aquifer responses. The results demonstrate strong predictive performance, with R2 = 0.851 for the testing data set. Cross-validation results further indicated stable model performance (R2 = 0.833 ± 0.018). Feature importance analysis revealed that groundwater pumping and recharge lag variables were the most influential factors controlling groundwater level variations. In addition, residual analysis and spatial error mapping were conducted to evaluate model uncertainty, while SHAP analysis was used to interpret the influence of input variables on groundwater level predictions. This study provides new insights into the dynamics of confined aquifer systems using machine-learning techniques and offers valuable information to support sustainable groundwater management in the study area.