Summary of Monitoring Water Contaminants in Coastal Areas Through Ml Algorithms Leveraging Atmospherically Corrected Sentinel-2 Data, by Francesca Razzano et al.
Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data
by Francesca Razzano, Francesco Mauro, Pietro Di Stasio, Gabriele Meoni, Marco Esposito, Gilda Schirinzi, Silvia Liberata Ullo
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Innovative Machine Learning Method Combines High-Resolution Data and CatBoost Algorithm to Accurately Monitor Turbidity Contaminants in Water, Enhancing Public Health and Environmental Protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Water contamination is a major concern that affects both human health and the environment. One key problem is turbidity, which can be difficult to monitor accurately. A new study uses a combination of machine learning (ML) and high-resolution data from Sentinel-2 Level-2A to track turbidity contaminants. The CatBoost ML algorithm is used to improve predictive accuracy. This approach is more efficient than traditional methods and provides scalable and precise monitoring results. The study also includes data from Hong Kong’s contaminant monitoring stations, providing insights into region-specific issues. |
Keywords
* Artificial intelligence * Machine learning
