Predicting Chlorophyll‑a Concentrations in the World’s Largest Lakes Using Kolmogorov-Arnold Networks

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator of eutrophication, is essential for the sustainable management of lake ecosystems. This study evaluated the performance of Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) and...

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Bibliographic Details
Published in:	Environmental science & technology 2025-01, Vol.59 (3), p.1801-1810
Main Authors:	Saravani, Mohammad Javad, Noori, Roohollah, Jun, Changhyun, Kim, Dongkyun, Bateni, Sayed M., Kianmehr, Peiman, Woolway, Richard Iestyn
Format:	Article
Language:	English
Subjects:	Algorithms Aquatic ecosystems Chlorophyll Chlorophyll A Ecosystem management Environmental Monitoring Eutrophication Lakes Learning algorithms Machine Learning Neural networks Neural Networks, Computer Nonlinear systems Occurrence, Fate, and Transport of Aquatic and Terrestrial Contaminants Performance evaluation prediction Predictions remote sensing Sustainability management Theorems Time series time series analysis
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