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Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

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第一作者： Seyed Vahid,Razavi-Termeh

第一单位： Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Electronic address: razavi@sejong.ac.kr.

作者： Seyed Vahid,Razavi-Termeh ^[1] ; Abolghasem,Sadeghi-Niaraki ^[2] ; X Angela,Yao ^[3] ; Rizwan Ali,Naqvi ^[4] ; Soo-Mi,Choi ^[5]

作者单位： Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Electronic address: razavi@sejong.ac.kr. ^[1] Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Electronic address: a.sadeghi@sejong.ac.kr. ^[2] Department of Geography, University of Georgia, Athens, GA, 30602, USA. Electronic address: xyao@uga.edu. ^[3] Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Republic of Korea. Electronic address: rizwanali@sejong.ac.kr. ^[4] Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Electronic address: smchoi@sejong.ac.kr. ^[5]

关键词 Explainable artificial intelligence Geospatial artificial intelligence Metaheuristic algorithms Noise pollution Spatially explicit

医学主题词人工智能(Artificial Intelligence);算法(Algorithms);噪声(Noise);伊朗(Iran);环境监测(Environmental Monitoring)

DOI 10.1016/j.jenvman.2024.122361

PMID 39255573

发布时间 2024-11-16

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