Monitoring Water Contaminants in Coastal Areas Through ML Algorithms Leveraging Atmospherically Corrected Sentinel-2 Data
Francesca Razzano (Università degli Studi del Sannio, Università degli Studi di Napoli Parthenope)
Francesco Mauro (Università degli Studi del Sannio)
Pietro Di Stasio (Università degli Studi del Sannio)
G. Meoni (Space Systems Egineering)
Marco Esposito (Cosine)
Gilda Schirinzi (Università degli Studi di Napoli Parthenope)
Silvia Liberata Ullo (Università degli Studi del Sannio)
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Abstract
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.