Data-Driven Approaches to Water Quality Monitoring: Leveraging AI, Machine Learning, and Management Strategies for Environmental Protection

Abstract

Water quality monitoring is essential to environmental protection, public health, and the sustainable use of water resources. Typically, traditional monitoring methods are not real time adaptable nor have high predictive accuracy. Therefore, this research investigates data driven approaches based on artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT) architecture to improve water quality assessment. With the purpose of predicting and analyzing levels of water pollution, the study implements four AI algorithms: Support Vector Machines (SVM), Decision Trees, Artificial Neural Networks (ANN), and Random Forests. Result from experimentation also shows ANN takes the highest accuracy of 95.2%, and Random Forests resulted at 92.8%, SVM 89.5%, and Decision Trees 87.3%. AI Driven Models resulted in reduction in error rate by 30%, better real time monitoring efficiency by 40%, and better contaminations detection. Comparative analysis with existing research demonstrates how hybrid AI models are more superior in terms of subject of predictive analytics. Nevertheless, they face challenges such as scalability and deployment in resource poor areas. Future research should be done on real time adaptive AI framework and the integration of large IoT. Based on these findings, the conclusion of this study is that AI-powered water quality monitoring provides a transformative solution for sustainable water management in order to make better decisions and save the environment.