Waste stabilization pond modelling using extreme gradient boosting machines

Abstract

The integrated solar and hydraulic jump-enhanced waste stabilization pond (ISHJEWSP) has been proposed as a solution to enhance performance of the conventional WSP. Despite the better performance of the ISHJEWSP, there is seemingly no previous study that has deployed machine learning (ML) methods in modelling the ISHJEWSP. This study is aimed at determining the relationships between the ISHJEWSP effluent parameters as well as comparing the performance of extra trees (ET), random forest (RF), decision tree (DT), light gradient boosting machine (LightGBM), gradient boosting (GB), and extreme gradient boosting (XGBoost) methods in predicting the effluent biochemical oxygen demand (BOD5) in the ISHJEWSP. The feature importance technique indicated that the most important were pH, temperature, solar radiation, dissolved oxygen (DO), and total suspended solids. These selected features yielded strong correlations with the dependent variable except DO, which had a moderate correlation. With respect to coefficient of determination and root mean square error (RMSE), the XGBoost performed better than the other models [coefficient of determination (R2) = 0.807, mean absolute error (MAE) = 4.3453, RMSE = 6.2934, root mean squared logarithmic error (RMSLE) = 0.1096]. Gradient boosting, XGBoost, and RF correspondingly yielded the least MAE, RMSE, and RMSLE of 3.9044, 6.2934, and 0.1059, respectively. The study demonstrates effectiveness of ML in predicting the effluent BOD5 in the ISHJEWSP.