Aqua Forecast: Machine Learning for Water Quality Prediction

An analysis of multiple bodies of water using ML algorithms is presented in the research paper. The primary focus is developing and evaluating predictive models for pH, dissolved oxygen, and turbidity: key water quality indicators. Various monitoring stations across a specific region collected comprehensive measurements of water quality. This research assesses the efficacy of some of the ML algorithms for predicting water quality parameters accurately. A rigorous test of the prediction performance of Decision Trees and K-Nearest Neighbours was conducted. It was found that KNN consistently outperformed DT in forecasting water quality parameters. In addition to providing greater insight into how water quality dynamics work, this study also offers practical implications for policymakers and researchers. KNN performs better than DT, demonstrating the importance of algorithm selection. Using KNN enhanced prediction accuracy, demonstrating its potential as a preferred algorithm. Using the latest ML technology for prediction of the water quality, this research provides valuable insights. The integration of ML techniques, especially KNN, can enhance the precision of water quality forecasting, providing crucial information for informed decision-making in the management of various bodies of water.