Abstract

This study investigates the use of machine learning models to predict total alkalinity (TA) based on chloride concentration (Cl-), pH and temperature. Utilizing RapidMiner's Auto Mode, six machine learning models were applied to a dataset of 111 water samples from the Nhà Bè River. The models' performances were evaluated using Root Mean Square Error (RMSE) and R² metrics, with the Generalized Linear Model (GLM), Support Vector Machine (SVM) and Deep Learning models identified as the top performers. Correlation and coefficient analyses revealed that Cl- had the most significant impact on TA prediction, followed by temperature and pH. These findings underscore the effectiveness of machine learning in water quality monitoring, presenting a cost-effective alternative to traditional chemical analysis methods.

Recommended citation

Tue Duy Nguyen, Quynh Thi Phuong Le, Man Thi Truc Doan, Ha Manh Bui (2024). Predicting Total Alkalinity in Saline Water Using Machine Learning: A Case Study with RapidMiner, Sustainable Chemistry One World . 100032, ISSN 2950-3574. https://doi.org/10.1016/j.scowo.2024.100032.