{"title":"应用模糊推理系统(FIS)和自适应神经模糊推理系统(ANFIS)技术建模悬浮粒子浓度:以地铁车站为例","authors":"Zahra Sadat Mousavi Fard, Hassan Asilian Mahabadi, Farahnaz Khajehnasiri, Mohammad Amin Rashidi","doi":"10.34172/ehem.2023.35","DOIUrl":null,"url":null,"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, PM2.5, PM10, 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 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, 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.","PeriodicalId":51877,"journal":{"name":"Environmental Health Engineering and Management Journal","volume":"29 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Zahra Sadat Mousavi Fard, Hassan Asilian Mahabadi, Farahnaz Khajehnasiri, Mohammad Amin Rashidi\",\"doi\":\"10.34172/ehem.2023.35\",\"DOIUrl\":null,\"url\":null,\"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, PM2.5, PM10, 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 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, 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.\",\"PeriodicalId\":51877,\"journal\":{\"name\":\"Environmental Health Engineering and Management Journal\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Health Engineering and Management Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34172/ehem.2023.35\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Health Engineering and Management Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34172/ehem.2023.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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
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, PM2.5, PM10, 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 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, 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.