An intelligent traceability method of water pollution based on dynamic multi-mode optimization

Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source’s characteristic information and provide technical support t...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 3; S. 2059 - 2076
Hauptverfasser:	Wu, Qinghua, Wu, Bin, Yan, Xuesong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.01.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convergence Data Mining and Knowledge Discovery Decision making Drinking water Emergency management Image Processing and Computer Vision Local optimization Optimization Pollution sources Probability and Statistics in Computer Science Problem solving Real time S.I. : Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Safety Special Issue on 2021 Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Technical services Water pollution Multi-mode optimization Dynamic Initialization strategies Simulation optimization Pollution intelligent traceability
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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