[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
近年来洪水引发的中小河流堤防溃决等洪水灾害风险问题凸显,因此进行溃堤洪水风险分析对于加强中小河流的洪水管理及减少溃堤洪水带来的损失具有十分重要的意义。以江西省罗塘河为例,借助MIKE软件中的MIKE 11、MIKE 21及其耦合模块对罗...近年来洪水引发的中小河流堤防溃决等洪水灾害风险问题凸显,因此进行溃堤洪水风险分析对于加强中小河流的洪水管理及减少溃堤洪水带来的损失具有十分重要的意义。以江西省罗塘河为例,借助MIKE软件中的MIKE 11、MIKE 21及其耦合模块对罗塘河遭遇10a一遇及20a一遇洪水进行溃堤洪水演进模拟。然后依据灾害系统理论从洪水的危险性和易损性两方面选择淹没水深、淹没流速、淹没历时等7个指标构建溃堤洪水风险评价指标体系。最后利用GIS技术与层次分析法对罗塘河洪水风险进行了评价。结果表明:洪水危险区面积为0.19 km 2,占研究区总面积的2.18%,主要分布在地势低洼的富港地区;重灾区和中灾区面积为1.25 km 2,占研究区总面积的14.37%,主要分布在重文和蒋元乐家;安全区为研究区域内洪水没有到达并且地物覆盖价值较低的地区,包括游家店、下胡、大塘杨家和马山等处。研究成果可为中小河流防洪规划、避洪转移等提供参考依据。展开更多
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
文摘近年来洪水引发的中小河流堤防溃决等洪水灾害风险问题凸显,因此进行溃堤洪水风险分析对于加强中小河流的洪水管理及减少溃堤洪水带来的损失具有十分重要的意义。以江西省罗塘河为例,借助MIKE软件中的MIKE 11、MIKE 21及其耦合模块对罗塘河遭遇10a一遇及20a一遇洪水进行溃堤洪水演进模拟。然后依据灾害系统理论从洪水的危险性和易损性两方面选择淹没水深、淹没流速、淹没历时等7个指标构建溃堤洪水风险评价指标体系。最后利用GIS技术与层次分析法对罗塘河洪水风险进行了评价。结果表明:洪水危险区面积为0.19 km 2,占研究区总面积的2.18%,主要分布在地势低洼的富港地区;重灾区和中灾区面积为1.25 km 2,占研究区总面积的14.37%,主要分布在重文和蒋元乐家;安全区为研究区域内洪水没有到达并且地物覆盖价值较低的地区,包括游家店、下胡、大塘杨家和马山等处。研究成果可为中小河流防洪规划、避洪转移等提供参考依据。
