Dynamic surface river pollution identification by a hybrid multivariate-based anomaly detection algorithm-Reference-Cited by-同舟云学术

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Dynamic surface river pollution identification by a hybrid multivariate-based anomaly detection algorithm

Published:2024-08 Issue: Volume:467 Page:142923
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Jiang Dexun,Zhu Hao,Liu Jie,Feng Xiaoxiao,Ma Fangjingxin,Wang Jing^ORCID

Funder

Postdoctoral Scientific Research Development Fund of Heilongjiang Province

China Postdoctoral Science Foundation

Heilongjiang Province Natural Science Foundation

China Scholarship Council

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference42 articles.

1. Machine learning for anomaly detection in cyanobacterial fluorescence signals;Almuhtaram;Water Res.,2021

2. Water characterization and early contamination detection in highly varying stochastic background water, based on Machine Learning methodology for processing real-time UV-Spectrophotometry;Arnon;Water Res.,2019

3. DeepSense: a Physics-Guided deep learning Paradigm for anomaly detection in Soil gas data at Geologic CO2 Storage sites;Bakhshian;Environ. Sci. Technol.,2021

4. An analysis of the relation between water pollution and economic growth in China by considering the contemporaneous correlation of water pollutants;Cai;J. Clean. Prod.,2020

5. Prediction of rural domestic water and sewage production based on automated machine learning in northern China;Cao;J. Clean. Prod.,2024

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