A paleo‐hydro‐geomorphological perspective on urban flood risk assessment

2019 ◽  
Vol 33 (25) ◽  
pp. 3169-3183
Author(s):  
Oscar Link ◽  
Luis M. Brox‐Escudero ◽  
John González ◽  
Mauricio Aguayo ◽  
Fernando Torrejón ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Flood Risk Assessment ◽  
Urban Flood ◽  
Urban Flood Risk

scholarly journals Ocean tide modelling for urban flood risk assessment in the Mekong Delta

2016 ◽  
Vol 10 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Hiroshi Takagi ◽  
Chiaki Tsurudome ◽  
Nguyen Danh Thao ◽  
Le Tuan Anh ◽  
Tran Van Ty ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Mekong Delta ◽  
Ocean Tide ◽  
Flood Risk Assessment ◽  
Urban Flood ◽  
Urban Flood Risk

Urban flood risk assessment using storm characteristic parameters sensitive to catchment-specific drainage system

2019 ◽  
Vol 659 ◽  
pp. 1362-1369 ◽  
Author(s):  
Yongchao Zhou ◽  
Dali Shen ◽  
Ning Huang ◽  
Yiping Guo ◽  
Tuqiao Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Drainage System ◽  
Flood Risk Assessment ◽  
Characteristic Parameters ◽  
Urban Flood ◽  
Urban Flood Risk

scholarly journals A Method for Urban Flood Risk Assessment and Zoning Considering Road Environments and Terrain

2019 ◽  
Vol 11 (10) ◽  
pp. 2734 ◽  
Author(s):  
Nengcheng Chen ◽  
Shuang Yao ◽  
Chao Wang ◽  
Wenying Du
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Disaster Response ◽  
Flood Risk Assessment ◽  
Urban Flood ◽  
Integrated Method ◽  
Risk Zones ◽  
Risk Grade ◽  
Artificial Neural Network Ann ◽  
Urban Flood Risk

Floods have been severely threatening social development worldwide. The occurrence of floods has multiple factors, and the flood risk considering road environments needs comprehensive analysis from meteorology, underlying surface, and urban road network. Thus, this study proposes an integrated method and constructs a road risk zoning model (RRZM). In the RRZM, submerged depth was obtained by the Soil Conservation Service (SCS) model, and the degree of road importance was obtained by the analytical hierarchy process (AHP) method. These two parts were used to characterize road vulnerability. Then the flood risk grade was evaluated based on the optimized artificial neural network (ANN). Finally, the results of flood risk assessment were obtained by road vulnerability and flood risk grade. The RRZM was applied to the Chang-Zhu-Tan Urban Agglomeration (CZTUA), China. The results showed that the spatial distributions of flood risk and the extent of road damage varied remarkably in different cities. Changsha was the most sensitive city to floods in the CZTUA. The flood risk zones were classified into six levels, and the vulnerable road sections identified from the risk zones at level 6 in the maps carried more traffic volume than others. By comparing with existing methods, it was found that the RRZM effectively reflected the spatial characteristics of flood risk considering road environments. It provides a new perspective for urban flood risk assessment and disaster response decision-making.

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