Prediction of Water Quality’s pH value using Random Forest and LightGBM Algorithms

Abstract

This study aims to compare Random Forest Regression and LightGBM algorithms for the prediction of pH value, which is an important parameter in water quality assessment. The performance of both algorithms is evaluated with metrics such as RMSE, R-squared and AUC (Area Under Curve). The results show that the LightGBM algorithm outperforms Random Forest (0.84) with an AUC value of 0.86 and provides better prediction accuracy, especially on large and complex datasets. These findings demonstrate the applicability of machine learning techniques in environmental monitoring processes and their potential for effective management of water quality. The results highlight the superiority of the LightGBM algorithm in solving environmental problems such as pH prediction, but also provide suggestions for more comprehensive approaches. The application of hybrid modeling techniques, generalizable analyses with datasets from different water sources, and the development of real-time monitoring systems are suggested to extend the findings of the study. This study contributes to the literature by demonstrating the importance of machine learning algorithms in environmental monitoring and water quality management.