Enhanced Hybrid Machine Learning Algorithm for Water Quality Assessment Using Decision Tree Regression Model - Mr. S. Uthayashangar M. Tech., PhD; Harini M; Dhivya B; Vaishnavi S; Shamshad Banu K (The Bioscan, 2026)
Environmental monitoring and public health depend on a good quality of water. Historically, conventional techniques like laboratory assessments and hand collecting have been used to evaluate water quality indicators. These methods do, however, have some restrictions in terms of time and space, a limited scope, and drawn-out procedures and expensive expenses. Previous studies have tried to increase estimate accuracy by merging Sentinel-2 remote sensing data with Fuzzy Similarity Analysis, therefore obtaining over 73% accuracy using Fuzzy Logic. Though progress in this area is commendable, real-time data processing and accurate prediction capability remain difficult. This work suggests a hybrid machine learning-driven approach to address these problems by means of Decision Tree Regression to evaluate water quality. Combining machine learning approaches with data from remote sensing this proposed system improves real-time monitoring efficacy and forecast accuracy. By means of state-of- the-art sensing technologies and data-driven algorithms, the framework detects minute variations in water quality parameters, hence improving water resource management decision-making.