# Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data

arXiv: https://arxiv.org/abs/2401.03792 · HTML: https://mlsanity.com/paper/2401.03792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis](https://mlsanity.com/paper/2301.07452/) (2023)

## Built on by (most cited first, 2 of 2)

- [Assessment of Spectral based Solutions for the Detection of Floating Marine Debris](https://mlsanity.com/paper/2408.10187/) (2024)
- [AquaCubeAI-Powered Monitoring Turbidity on-board Phisat-2](https://mlsanity.com/paper/2609.12744/) (2026)

## Abstract (arXiv)

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.