AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR WATER QUALITY PREDICTION

Abstract

Water is a vital natural resource that supports human health, agriculture, and industrial development, making the monitoring and management of water quality extremely important for sustainable environmental protection. However, increasing urbanization, industrial activities, and agricultural runoff have significantly contributed to the deterioration of water bodies, creating a need for intelligent and efficient monitoring systems. This study presents an intelligent machine learning framework for water quality prediction and monitoring that analyzes historical and real-time data to accurately assess the condition of water resources. The proposed framework incorporates data preprocessing, feature selection, and machine learning algorithms to evaluate important water quality parameters such as pH, dissolved oxygen, turbidity, and temperature. By identifying hidden patterns and relationships within the data, the model can effectively predict future water quality levels and detect potential contamination risks. The performance of the model is validated using appropriate evaluation techniques to ensure accuracy and reliability. Overall, the proposed framework offers an efficient and scalable solution for continuous water quality monitoring, helping environmental agencies and policymakers take proactive measures for sustainable water resource management and public health protection.