The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster...The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required. Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena. Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods.展开更多
This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network r...This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.展开更多
文摘The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required. Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena. Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods.
基金supported by the National Natural Science Foundation of China (Grant Nos.42271483,42071364)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (Grant No.KYCX23_1696).
文摘This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.
