Improving the Accuracy of Groundwater Level Forecasting by Coupling Ensemble Machine Learning Model and Coronavirus Herd Immunity Optimizer

Abstract

Groundwater levels are under severe pressure globally due to over-extraction, pollution, and climate change necessitating continuous monitoring for sustainable aquifer management. This study introduces a novel ensemble machine learning (En) model that integrates shallow and deep machine learning (ML) models, optimized through the coronavirus herd immunity optimizer (CHIO), for accurate groundwater level forecasting. This En model was applied to the Ergene River Basin, Turkiye, a region facing severe groundwater depletion and contamination due to intensive agricultural and industrial activities. Groundwater level data spanning 1966 to 2023 on a weekly basis from four wells were used, split into 70% for training and 30% for testing under short- and long-term scenarios. Using the partial autocorrelation function and gamma test the best lag numbers were determined for input data, reflecting aquifer heterogeneity. Score analysis, supported by statistical metrics such as the coefficient of determination (R-2) and root mean square error (RMSE), was employed alongside visual aids to assess the developed En model performance. Results demonstrated that deep ML models outperformed shallow ML models achieving R-2 similar to 0.99 and RMSE similar to 0.5 m. The developed En model outperformed all individual ML models, with score values exceeding 200, and its predictions closely aligned with measured water levels during both testing phases. The findings underscored the developed En model's contribution to achieving sustainable development goals (SDGs) by enhancing water-use efficiency and addressing environmental, economic, and social sustainability challenges. The proposed approach offers a reliable and adaptable solution for groundwater level forecasting, applicable to other aquifers worldwide.