Deciphering and predicting algal bloom variability using size-fractionated organic matter and machine learning in a complex watershed

Deciphering and predicting algal bloom variability using size-fractionated organic matter and machine learning in a complex watershed

Understanding the drivers of algal bloom variability is critical for managing eutrophication in freshwater reservoirs, yet most predictive models underrepresent the role of dissolved organic matter (DOM) composition. This study explores the contribution of size-fractionated DOM, specifically dissolv...

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Bibliographic Details
Main Authors: Yun Kyung Lee, Haeseong Oh, Bo-Mi Lee, Jin Hur
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Ecological Indicators
Subjects:
Agal bloom
Dissolved organic matter
Machine learning
Fluorescence spectroscopy
DOM size fractionation
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X25009999
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