A novel approach for prediction of groundwater quality using gradient boosting-based algorithms.

This study explores the potential of the GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid search, and Bayesian optimization were used to find the optimal values of various hyperparameters with all four-gradient boosting-based algorithms. One hundred and two samples of Entropy weighted water quality index with 14 input parameters are used for assessing groundwater quality. The calculated EWQI values for drinking range between 80.4 and 394.96 in pre-monsoon and 39.6 to 338.79 during the post-monsoon period. Moreover, spatial distribution maps displayed that the central portions of the study area fall under medium water quality. The performances of models were compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). The results reveal that the CC value by all modeling approaches is more than 0.93, suggesting a comparable performance by all methods. Results in terms of RMSE values in predicting the EWQI values suggest GPBoost (random search) model performed better than the other three models, thus suggesting a competitive performance by GPBoost in comparison to other gradient boosting-based approaches. Relative importance analysis provided by random and grid search methods highlights the significance of NO₃⁻, Mg²⁺, TDS, EC, and TH as important input parameters for predicting EWQI. Four gradient boosting-based machine-learning algorithms applied for EWQI prediction. GPBoost by random search performed better than the other three models. NO₃⁻, Mg²⁺, TDS, EC, and TH was most effective input parameters for predicting EWQI. Establishing a novel approach for estimating groundwater quality for drinking. [ABSTRACT FROM AUTHOR]