A Review Of Machine Learning And Flask For Water Quality Classification

Water quality deterioration poses a substantial environmental and health threat. Predicting water quality can be crucial for proactive management and mitigation strategies. This study explores the use of machine learning algorithms for water quality classification. The Water Quality Index (WQI), incorporating parameters like pH, temperature, and dissolved oxygen, will be used to evaluate water quality. Subsequently, water samples will be assigned distinct classes based on their WQI values. Machine learning algorithms, including K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest, will be implemented for classification. Their performance will be evaluated using accuracy, confusion matrix, precision, recall, and F1-score metrics. This study aims to identify the optimal machine learning algorithm for water quality classification, contributing to the development of efficient and reliable water quality monitoring systems.