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PM<sub>2.5</sub> concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

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第一作者： Qing,Wei

第一单位： College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, 200092, China.

作者： Qing,Wei ^[1] ; Huijin,Zhang ^[1] ; Ju,Yang ^[2] ; Bin,Niu ^[3] ; Zuxin,Xu ^[4]

作者单位： College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, 200092, China. ^[1] Guangdong Institute of Water Resources and Hydropower Research, Guangzhou, 510000, China. ^[2] PowerChina East China Survey, Design and Research Institute Co. Ltd., Hangzhou, 310000, China. ^[3] College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, 200092, China. Electronic address: zuxinxu@yeah.net. ^[4]

关键词 Convolutional neural network Long short-term memory PM(2.5) prediction Shapley additive explanation Whale optimization algorithm

医学主题词颗粒物(Particulate Matter);空气污染物(Air Pollutants);环境监测(Environmental Monitoring);空气污染(Air Pollution);算法(Algorithms);鲸(Whales);动物(Animals)

DOI 10.1016/j.envpol.2025.125953

PMID 40032225

发布时间 2025-05-10

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