Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain

Abstract

This paper presents a comprehensive study of various Random Forest (RF) models for predicting increments of pH in groundwater treated by permeable reactive barriers (PRBs). This study uses data collected over 16 years from two PRBs installed in acid sulfate soils. The hyperparameters of the Random Forest have been optimised using three powerful optimisation algorithms: the Whale Optimisation Algorithm, the Tunicate Swarm Algorithm, and the Jellyfish Search Optimizer. The results show that the RF-Jellyfish Search Optimiser was the most accurate model for predicting pH increment in the downgradient. This model could predict pH increments for 80 data points as well as another PRB with a different reactive material. The model was also compared with a finite difference flow model from literature. The results showed that the RF-Jellyfish Search Optimizer was more accurate with the coefficient of determination and root mean square error values of 0.954 and 7.04, compared to 0.942 and 10.27 obtained from the finite difference model. Overall, the novel intelligent technique introduced in this study effectively predicts pH increments in treated groundwater and can also be applied to different PRBs.