Using multiple machine learning algorithms to optimize the water quality index model and their applicability

Using multiple machine learning algorithms to optimize the water quality index model and their applicability

Water quality assessment model and spatiotemporal heterogeneity pose challenges to the uncertainty of water quality assessment. To improve the accuracy of the water quality index (WQI) model, multiple machine learning algorithms (CatBoost, SVM, LR, XGBoost, LightGBM) and entropy weight method (EWM)...

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Bibliographic Details
Main Authors: Fei Ding, Shilong Hao, Wenjie Zhang, Mingcen Jiang, Liangyao Chen, Haobin Yuan, Nan Wang, Wenpan Li, Xin Xie
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Ecological Indicators
Subjects:
Water quality assessment
Machine learning
Applicability
Game theory
Water quality index
Chaohu lake
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X25002304

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http://www.sciencedirect.com/science/article/pii/S1470160X25002304

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