Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.03792 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914634014916608 |
|---|---|
| author | Razzano, Francesca Mauro, Francesco Di Stasio, Pietro Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia Liberata |
| author_facet | Razzano, Francesca Mauro, Francesco Di Stasio, Pietro Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia Liberata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data Razzano, Francesca Mauro, Francesco Di Stasio, Pietro Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia Liberata Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.03792 |