Nutrient Pollution

Quadra, Gabrielle Rabelo; Brovini, Emília Marques

doi:10.1007/978-3-030-38948-2_60-1

The Palgrave Handbook of Global Sustainability 2022

DOI: 10.1007/978-3-030-38948-2_60-1

|View full text |Cite

Nutrient Pollution

Gabrielle Rabelo Quadra

Emília Marques Brovini

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2025

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Guansan,

Avtar,

Meraj

et al. 2025

Water

View full text Add to dashboard Cite

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

show abstract

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

Guansan,

Avtar,

Meraj

et al. 2025

Water

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Nutrient Pollution

Cited by 1 publication

References 65 publications

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

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

Contact Info

Product

Resources

About