A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Constructing a Machine Learning Model for Rapid Urban Flooding Forecast in Sloping Cities along the Yangtze River: A Case Study in Jiujiang
Cities with sloping terrain are more susceptible to flooding during heavy rains. Traditional hydraulic models struggle to meet computational demands when addressing such emergencies. This study presented an integration of the one-dimensional Storm Water Management Model (SWMM) and the two-dimensional LISFLOOD-FP model, where the head difference at coupled manholes between the two models functioned as the connection. Based on its calculation results, this study extracted the characteristic parameters of the rainfall data, simplified the SVR calculation method and developed a high-efficiency solution for determining the maximum ponding depth. The cost time of this model was stable at approximately 1.0 min, 95% faster compared to the one from the mechanism model for 5 h simulation under the same working conditions. By conducting this case study in Jiujiang, China, the feasibility of this algorithm was well demonstrated.
Constructing a Machine Learning Model for Rapid Urban Flooding Forecast in Sloping Cities along the Yangtze River: A Case Study in Jiujiang
Cities with sloping terrain are more susceptible to flooding during heavy rains. Traditional hydraulic models struggle to meet computational demands when addressing such emergencies. This study presented an integration of the one-dimensional Storm Water Management Model (SWMM) and the two-dimensional LISFLOOD-FP model, where the head difference at coupled manholes between the two models functioned as the connection. Based on its calculation results, this study extracted the characteristic parameters of the rainfall data, simplified the SVR calculation method and developed a high-efficiency solution for determining the maximum ponding depth. The cost time of this model was stable at approximately 1.0 min, 95% faster compared to the one from the mechanism model for 5 h simulation under the same working conditions. By conducting this case study in Jiujiang, China, the feasibility of this algorithm was well demonstrated.
Constructing a Machine Learning Model for Rapid Urban Flooding Forecast in Sloping Cities along the Yangtze River: A Case Study in Jiujiang
Zhong Gao (author) / Xiaoping Lu (author) / Ruihong Chen (author) / Minrui Guo (author) / Xiaoxuan Wang (author)
2024
Article (Journal)
Electronic Resource
Unknown
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