A Resilient Water Quality Investigation System using Support Vector Machines and Intelligent Feature Engineering

Ensuring water quality is vital for environmental monitoring, essential in protecting public health and ecological balance. The global decline in water resources underscores the need for intelligent strategies to thoroughly assess and manage water quality. Support Vector Machine (SVM), a robust machine learning algorithm, have captured the interest of researchers for their potential in addressing the complexities of water quality datasets. Unlike conventional methods, SVM excels in identifying intricate patterns within data, making it well-suited for the multifaceted nature of water quality parameters. The study introduces the SVM-based Resilient Water Quality Investigation System (SVM-RWQIS), employing an integrated approach that strategically combines SVM techniques with advanced feature engineering. Emphasizing the pivotal role of feature engineering, the research enhances the model's capacity to extract meaningful insights from raw data. This synergistic collaboration not only enhances the proposed model's performance but also deepens our understanding of intricate relationships among diverse water quality factors. The findings underscore SVM's superiority, showcasing its potential to surpass conventional techniques in capturing dynamic and non-linear relationships inherent in water quality data. This study offers a practical framework for proactive water quality management, benefiting policymakers, environmental scientists, and water resource managers amid escalating environmental challenges.