{"title":"利用社会指标，基于多层感知器预测火灾发生率","authors":"Chu Zhang, Won-Hwa Hong, Young-Hoon Bae","doi":"10.7731/kifse.9ccb98be","DOIUrl":null,"url":null,"abstract":"To analyze the impact of urban socioeconomic and demographic factors on fire occurrences and predict fire occurrences according to each factor, this study analyzed the correlation between “Korean social indicators” and fire occurrences. Based on this, a fire prediction model was built based on multi-layer perceptron (MLP). For this purpose, data on social indicators and the number of fires by city, county, and district from 2015 to 2022 were collected, and the correlation between social indicators and fire occurrences were analyzed. Based on the correlation analysis results, two models were built to predict fires using 15 factors (Model 1) and 5 factors (Model 2). The mean absolute percentage error of the models were 26.37% (Model 1) and 30.92% (Model 2), confirming the usability of the fire prediction model based on the multi-layer perceptron using social indicators.","PeriodicalId":514545,"journal":{"name":"Fire Science and Engineering","volume":"31 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Fire Occurrences Based on Multi-layer Perceptron Using Social Indicators\",\"authors\":\"Chu Zhang, Won-Hwa Hong, Young-Hoon Bae\",\"doi\":\"10.7731/kifse.9ccb98be\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To analyze the impact of urban socioeconomic and demographic factors on fire occurrences and predict fire occurrences according to each factor, this study analyzed the correlation between “Korean social indicators” and fire occurrences. Based on this, a fire prediction model was built based on multi-layer perceptron (MLP). For this purpose, data on social indicators and the number of fires by city, county, and district from 2015 to 2022 were collected, and the correlation between social indicators and fire occurrences were analyzed. Based on the correlation analysis results, two models were built to predict fires using 15 factors (Model 1) and 5 factors (Model 2). The mean absolute percentage error of the models were 26.37% (Model 1) and 30.92% (Model 2), confirming the usability of the fire prediction model based on the multi-layer perceptron using social indicators.\",\"PeriodicalId\":514545,\"journal\":{\"name\":\"Fire Science and Engineering\",\"volume\":\"31 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fire Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7731/kifse.9ccb98be\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fire Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7731/kifse.9ccb98be","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Fire Occurrences Based on Multi-layer Perceptron Using Social Indicators
To analyze the impact of urban socioeconomic and demographic factors on fire occurrences and predict fire occurrences according to each factor, this study analyzed the correlation between “Korean social indicators” and fire occurrences. Based on this, a fire prediction model was built based on multi-layer perceptron (MLP). For this purpose, data on social indicators and the number of fires by city, county, and district from 2015 to 2022 were collected, and the correlation between social indicators and fire occurrences were analyzed. Based on the correlation analysis results, two models were built to predict fires using 15 factors (Model 1) and 5 factors (Model 2). The mean absolute percentage error of the models were 26.37% (Model 1) and 30.92% (Model 2), confirming the usability of the fire prediction model based on the multi-layer perceptron using social indicators.
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