Machine Learning Techniques for Water Quality Classification of Thailand's Rivers

Abstract

Water is necessary for human consumption. To ensure that water is safe, a monitoring system for water quality is required. One part of the system is to be able to predict the water quality class. Using data collected from the Pollution Control Department of Thailand from 2009 to 2021, we compared four machine learning approaches for classifying water quality classes in four main rivers in Thailand: the Ping, Wang, Yom, and Nan rivers. Random Forest, Extreme Gradient Boosting (XGBoost), Logistic Regression, and Support Vector Machine were used in this study. Moreover, synthetic minority oversampling technique (SMOTE) and Random oversampling, two strategies for dealing with imbalanced data, were also used to improve classification F1 score. This study found that XGBoost with SMOTE achieved the highest score, and BOD was the most important feature in classifying water quality. © 2023 IEEE.