Modeling the concentration of suspended particles by fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) techniques: A case study in the metro stations
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_EHEM-10-3_009
تاریخ نمایه سازی: 20 شهریور 1402
Abstract:
Background: Today, the usage of artificial intelligence systems and computational intelligence is increasing. This study aimed to determine the fuzzy system algorithms to model and predict the amount of air pollution based on the measured data in subway stations.
Methods: In this study, first, the effective variables on the concentration of particulate matter were determined in metro stations. Then, PM۲.۵, PM۱۰, and total size particle (TSP) concentrations were measured. Finally, the particles’ concentration was modeled using fuzzy systems, including the fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS).
Results: It was revealed that FIS with modes gradient segmentation (FIS-GS) could predict ۷۶% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict ۸۵% of PM۲.۵, PM۱۰, and TSP particle concentrations.
Conclusion: According to the results, among the models studied in this work, ANFIS-FCM-CPDTA, due to its better ability to extract knowledge and ambiguous rules of the fuzzy system, was considered a suitable model.
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Authors
Zahra Sadat Mousavi Fard
Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
Hassan Asilian Mahabadi
Corresponding author: Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
Farahnaz Khajehnasiri
Department of Community Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Mohammad Amin Rashidi
Student Research Committee, Department of Occupational Health and Safety, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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