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arXiv · 2401.03792

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
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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.

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Francesca Razzano, Francesco Mauro, Pietro Di Stasio, Gabriele Meoni, Marco Esposito, Gilda Schirinzi, Silvia Liberata Ullo. 2024-01-08. Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data. https://arxiv.org/abs/2401.03792

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