Estimation of Manganese Content in Potable Water by Boosting Techniques

Abstract In this study, boosting machine learning techniques were employed to estimate the concentration of manganese in the potable water of Yuvacık Dam and to predict the days that surpassed and fell below the threshold value of 0.05 mg/L in Turkey. We conducted both regression and classification analyses for the same issue. We also implemented sampling methods when the data distribution in the classification task became imbalanced. We obtained daily measurements for approximately seven years, from 2004 to 2011, to build the dataset, which consisted of seven columns in total. While the XGBoost algorithm forecasted the manganese content in potable water with a mean absolute error of 0.0055, the Light GBM algorithm predicted the days with elevated manganese levels with an accuracy of 0.97. The models’ high predictions allow us to adjust the frequency of frequent water sampling and lab analysis and take prompt action during water filtration processes.