Analysis of Water Quality for Taal Lake Using Machine Learning Classification Algorithm

The rapid population growth in the Philippines has increased the demand for food and aquatic commodities, making fishing a crucial source of income for coastal households [1]. Ensuring the water quality of Philippine lakes is essential to maintaining the environment's integrity and protecting communities that rely on aquatic resources. In this work, we applied machine learning classification algorithms such as Random Forest, Decision Tree, and Support Vector to calculate Taal Lake, Philippines's Water Quality Index (WQI) and Water Quality Classification (WQC). The Weighted Arithmetic Water Quality Index (WAWQI) approach was employed to classify the water quality in Taal Lake. Our results showed the lake's water quality was unsuitable between 2018 and 2022 at five selected stations. Moreover, we evaluated the classification model against two other algorithms and demonstrated that it outperformed Precision, Recall, and the F-1 score. Random Forest achieved the highest overall accuracy rate of 95.0% compared to the other models tested. This study emphasizes the importance of utilizing machine learning algorithms to monitor and classify water quality in the Philippines.