Forecasting short-term chlorophyll a concentration in Lake Erie using the machine learning XGBoost algorithm

Harmful algal blooms are becoming increasingly prevalent due to climate warming and eutrophication. Leveraging machine learning tools to forecast algal blooms is crucial and promising for bloom management in various water systems. Notably, previous findings are site-specific, especially regarding th...

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Bibliographic Details
Published in:	Environmental research letters Vol. 20; no. 6; pp. 64029 - 64038
Main Author:	Song, Yang
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.06.2025
Subjects:	Accuracy Algae Algal blooms Algorithms Aquatic ecosystems artificial intelligence Biomass Chlorophyll Climate change Early warning systems Eutrophication feature importance Forecasting forecasting technique Global warming Lakes Learning algorithms Machine learning Nitrogen Nutrients Organic nitrogen Organic phosphorus Particulate organic nitrogen Particulate organic phosphorus phytoplankton Resource allocation Water depth Water quality Water quality measurements Water temperature Lake Erie
ISSN:	1748-9326, 1748-9326
Online Access:	Get full text
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