{"title":"利用三维城市模型确定洪水灾害对道路网络影响的新方法","authors":"R. Khalil","doi":"10.21608/jesaun.2022.140556.1140","DOIUrl":null,"url":null,"abstract":"Flash flood in urban area strikes mainly the road network. In fact, the streets during flood act as streams or overland flow paths. This jams the traffic, stops the public services, and interrupts the economic activities. Previous studies have treated floods in urban areas as if they were occurring in rural areas. This study presents a new approach that treats the road network as the path of the flash flood water. The new approach uses a 3D city model as the basis for hydrology analysis. This approach regards the building and the streets as part of the terrain that results in water flowing through the streets as it does in reality. The depth of flood water in the streets is calculated and used as a risk factor. Remote Sensing (RS) and Geographical Information System (GIS) technologies are used to obtain and prepare the required input data for the hydraulic model. Various flood scenarios were investigated for different return periods and flood risk code maps for the road network were generated. The obtained results showed that 41.2% of the road network in the study area is under high flood risk from fairly frequent rainfall events, and this percentage reaches 80% to 90% for low frequent flood events (50 years and 100 years flood). The new approach was evaluated by comparing the derived results with actual flood data and had an accuracy of 77%. The results of this study may help decision makers to take the necessary actions to protect people and property.","PeriodicalId":166670,"journal":{"name":"JES. Journal of Engineering Sciences","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new approach to determine the flood hazard impact on road network using 3D city model\",\"authors\":\"R. Khalil\",\"doi\":\"10.21608/jesaun.2022.140556.1140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flash flood in urban area strikes mainly the road network. In fact, the streets during flood act as streams or overland flow paths. This jams the traffic, stops the public services, and interrupts the economic activities. Previous studies have treated floods in urban areas as if they were occurring in rural areas. This study presents a new approach that treats the road network as the path of the flash flood water. The new approach uses a 3D city model as the basis for hydrology analysis. This approach regards the building and the streets as part of the terrain that results in water flowing through the streets as it does in reality. The depth of flood water in the streets is calculated and used as a risk factor. Remote Sensing (RS) and Geographical Information System (GIS) technologies are used to obtain and prepare the required input data for the hydraulic model. Various flood scenarios were investigated for different return periods and flood risk code maps for the road network were generated. The obtained results showed that 41.2% of the road network in the study area is under high flood risk from fairly frequent rainfall events, and this percentage reaches 80% to 90% for low frequent flood events (50 years and 100 years flood). The new approach was evaluated by comparing the derived results with actual flood data and had an accuracy of 77%. The results of this study may help decision makers to take the necessary actions to protect people and property.\",\"PeriodicalId\":166670,\"journal\":{\"name\":\"JES. Journal of Engineering Sciences\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JES. Journal of Engineering Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/jesaun.2022.140556.1140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JES. Journal of Engineering Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/jesaun.2022.140556.1140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new approach to determine the flood hazard impact on road network using 3D city model
Flash flood in urban area strikes mainly the road network. In fact, the streets during flood act as streams or overland flow paths. This jams the traffic, stops the public services, and interrupts the economic activities. Previous studies have treated floods in urban areas as if they were occurring in rural areas. This study presents a new approach that treats the road network as the path of the flash flood water. The new approach uses a 3D city model as the basis for hydrology analysis. This approach regards the building and the streets as part of the terrain that results in water flowing through the streets as it does in reality. The depth of flood water in the streets is calculated and used as a risk factor. Remote Sensing (RS) and Geographical Information System (GIS) technologies are used to obtain and prepare the required input data for the hydraulic model. Various flood scenarios were investigated for different return periods and flood risk code maps for the road network were generated. The obtained results showed that 41.2% of the road network in the study area is under high flood risk from fairly frequent rainfall events, and this percentage reaches 80% to 90% for low frequent flood events (50 years and 100 years flood). The new approach was evaluated by comparing the derived results with actual flood data and had an accuracy of 77%. The results of this study may help decision makers to take the necessary actions to protect people and property.