Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	The Science of the total environment 2023-11, Vol.901, p.166467-166467, Article 166467
Hauptverfasser:	Ly, Quang Viet, Tong, Ngoc Anh, Lee, Bo-Mi, Nguyen, Minh Hieu, Trung, Huynh Thanh, Le Nguyen, Phi, Hoang, Thu-Huong T., Hwang, Yuhoon, Hur, Jin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algal bloom Fluorescence Machine learning Spectroscopy Water pollution
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!