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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Vydáno v:	Environmental research letters Ročník 20; číslo 6; s. 64029 - 64038
Hlavní autor:	Song, Yang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bristol IOP Publishing 01.06.2025
Témata:	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
On-line přístup:	Získat plný text
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