Integrating Remote Sensing and Machine Learning for Dynamic Monitoring of Eutrophication in River Systems: A Case Study of Barato River, Japan

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 89 - 89

Published: Jan. 1, 2025

Rivers play a crucial role in nutrient cycling, yet are increasingly affected by eutrophication due to anthropogenic activities. This study focuses on the Barato River Hokkaido, Japan, employing an integrated approach of field measurements and Sentinel-2 satellite remote sensing monitor as river experiencing huge sewage effluents. Key parameters such chlorophyll-a (Chla), dissolved inorganic nitrogen (DIN), phosphorus (DIP), Secchi Disk Depth (SDD) were analyzed. The developed empirical models showed strong predictive capability for water quality, particularly Chla (R2 = 0.87), DIP 0.61), SDD 0.82). Seasonal analysis indicated peak concentrations October, reaching up 92.4 μg/L, alongside significant decreases DIN DIP, suggesting high phytoplankton activity. Advanced machine learning models, specifically back propagation neural networks, improved prediction accuracy with R2 values 0.90 0.83 DIN. Temporal analyses from 2018 2022 consistently revealed River’s eutrophic state, severe occurring 33% year moderate over 50%, emphasizing ongoing imbalance. correlation between highlights main driver eutrophication. These findings demonstrate efficacy integrating dynamic monitoring eutrophication, providing critical insights management quality improvement.

Language: Английский

Interpretable machine learning reveals the importance of geography and landscape arrangement for surface water quality across China

Water Research, Journal Year: 2025, Volume and Issue: unknown, P. 123578 - 123578

Published: March 1, 2025

Language: Английский