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.

翻译：水污染物监测对于保障公众健康和环境福祉至关重要。浊度作为关键参数，其污染问题严重影响水质。准确评估浊度对于保护生态系统和人类饮用水安全具有决定性意义，需要采取严谨细致的监测行动。为此，本研究开创性地提出一种整合CatBoost机器学习与Sentinel-2 Level-2A高分辨率数据的新型浊度污染物监测方法。传统方法劳动强度大，而CatBoost凭借其卓越的预测精度提供了高效解决方案。通过谷歌地球引擎（GEE）利用大气校正后的Sentinel-2数据，本研究为可扩展的高精度浊度监测做出贡献。基于香港污染物监测站构建的专用表格数据集，为研究提供了区域特异性见解。实验结果表明该集成方法具有可行性，为全球水质管理领域采用先进技术奠定了基础。