Water Quality Prediction Using Machine Learning with Hydrologic factors and Satellite Imagery Integration

High concentrations of chlorophyll-a (Chl-a) in aquatic systems pose serious environmental and public health concerns. Chl-a, a primary marker of phytoplankton biomass, is often associated with the proliferation of harmful algal blooms (HABs). These blooms produce toxins that not only threaten marine organisms but also have far-reaching impacts on human health and aquatic ecosystems. These toxins can degrade water quality, disrupt food webs, and result in significant fish mortality. When these harmful substances contaminate drinking water sources, they can cause a range of health problems, from short-term illnesses to chronic diseases.

Despite the importance of predicting Chl-a levels, earlier research has largely focused on water quality parameters without adequately considering the dynamic nature of river hydrology. This study bridges that gap by leveraging satellite data to enhance predictive accuracy. Sentinel-2 imagery was utilized to monitor water quality, while Sentinel-1 data captured the hydrological characteristics of rivers. To forecast Chl-a, four machine learning models were deployed, with their performance evaluated through Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) metrics. Additionally, the study used Shapley Additive Explanations (SHAP) to unravel the contribution of individual water quality variables and satellite-derived data to the prediction process.

By integrating hydrological factors with water quality predictions, this research provides a more holistic understanding of river systems. Such insights are vital for optimizing the operation of water management structures like dams and weirs. Moreover, the incorporation of retention time analysis offers a proactive approach to monitoring and preventing HABs, enabling more effective management of aquatic ecosystems under varying environmental conditions worldwide.