{"title":"开发和实施用于模拟自然区域洪水现象的人工神经网络","authors":"E. Keramaris, Ioannis Petikas","doi":"10.4028/p-yos9a2","DOIUrl":null,"url":null,"abstract":"In this study an Artificial Neural Network for the simulation of flood phenomena in a natural area was developed. Then this network was implemented in the urban area of a Greek city (Amyntaio, Florina). The neural networks have many advantages: non-linearity, adaptability, input-output mapping, indicative response, damage resistance, possibility of implementation with VLSI (Very Large Scale Integration) technology, content related information and analysis and design uniformity. With neural networks, mathematical simulation of the considered phenomenon is not attempted, but the extraction of quantitative conclusions for specific data, based on similar cases. With the development and implementation of this network all the points that are in risk for flood are identified. The results showed that the help of an Artificial Neural Network in these cases is crucial for the future decisions in cases of flood phenomena.","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":"3 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Implementation of an Artificial Neural Network for the Simulation of Flood Phenomena in a Natural Area\",\"authors\":\"E. Keramaris, Ioannis Petikas\",\"doi\":\"10.4028/p-yos9a2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study an Artificial Neural Network for the simulation of flood phenomena in a natural area was developed. Then this network was implemented in the urban area of a Greek city (Amyntaio, Florina). The neural networks have many advantages: non-linearity, adaptability, input-output mapping, indicative response, damage resistance, possibility of implementation with VLSI (Very Large Scale Integration) technology, content related information and analysis and design uniformity. With neural networks, mathematical simulation of the considered phenomenon is not attempted, but the extraction of quantitative conclusions for specific data, based on similar cases. With the development and implementation of this network all the points that are in risk for flood are identified. The results showed that the help of an Artificial Neural Network in these cases is crucial for the future decisions in cases of flood phenomena.\",\"PeriodicalId\":512976,\"journal\":{\"name\":\"Engineering Headway\",\"volume\":\"3 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Headway\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4028/p-yos9a2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Headway","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-yos9a2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development and Implementation of an Artificial Neural Network for the Simulation of Flood Phenomena in a Natural Area
In this study an Artificial Neural Network for the simulation of flood phenomena in a natural area was developed. Then this network was implemented in the urban area of a Greek city (Amyntaio, Florina). The neural networks have many advantages: non-linearity, adaptability, input-output mapping, indicative response, damage resistance, possibility of implementation with VLSI (Very Large Scale Integration) technology, content related information and analysis and design uniformity. With neural networks, mathematical simulation of the considered phenomenon is not attempted, but the extraction of quantitative conclusions for specific data, based on similar cases. With the development and implementation of this network all the points that are in risk for flood are identified. The results showed that the help of an Artificial Neural Network in these cases is crucial for the future decisions in cases of flood phenomena.