Monitoring water contaminants in coastal areas through ML algorithms
  leveraging atmospherically corrected Sentinel-2 data
Francesca Razzano Francesco Mauro Pietro Di Stasio Gabriele Meoni Marco Esposito Gilda Schirinzi Silvia Liberata Ullo
Monitoring water contaminants is of paramount importance, ensuring public
health and environmental well-being. Turbidity, a key parameter, poses a
significant problem, affecting water quality. Its accurate assessment is
crucial for safeguarding ecosystems and human consumption, demanding meticulous
attention and action. For this, our study pioneers a novel approach to monitor
the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with
high-resolution data from Sentinel-2 Level-2A. Traditional methods are
labor-intensive while CatBoost offers an efficient solution, excelling in
predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data
through the Google Earth Engine (GEE), our study contributes to scalable and
precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong
contaminants monitoring stations enriches our study, providing region-specific
insights. Results showcase the viability of this integrated approach, laying
the foundation for adopting advanced techniques in global water quality
management.