For the challenge of parameter calibration in the process of SWMM(storm water management model)model application,we use particle Swarm Optimization(PSO)and Sequence Quadratic Programming(SQP)in combination to calibrat...For the challenge of parameter calibration in the process of SWMM(storm water management model)model application,we use particle Swarm Optimization(PSO)and Sequence Quadratic Programming(SQP)in combination to calibrate the parameters and get the optimal parameter combination in this research.Then,we compare and analyze the simulation result with the other two respectively using initial parameters and parameters obtained by PSO algorithm calibration alone.The result shows that the calibration result of PSO-SQP combined algorithm has the highest accuracy and shows highly consistent with the actual situation,which provides a scientific and effective new idea for parameter calibration of SWMM model,moreover,has practical guidance for flood control and disaster mitigation.展开更多
In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines,...In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines, forming the Geographical WeightedRegression (GWR). Based on classic statistical techniques, GWR model has ascertainsignificance in solving spatial dependence and spatial non-uniform problems, but it hasno impact on the integration of the scale effect. It does not consider the interactionbetween the various factors of the sampling scale observations and the numerous factorsof possible scale effects, so there is a loss of information. Crossing a two-stage analysisof “return of regression” to establish the model of Hierarchical Geographically WeightedRegression (HGWR), the first layer of regression analysis reflects the spatial dependenceof space samples and the second layer of the regression reflects the spatial relationshipsscaling. The combination of both solves the spatial scale effect analysis, spatialdependence and spatial heterogeneity of the combined effects.展开更多
基金We would like to express our acknowledgements to the Fund of postgraduate training and innovation project of Jiangsu Province(NO.SJKY19_0969).
文摘For the challenge of parameter calibration in the process of SWMM(storm water management model)model application,we use particle Swarm Optimization(PSO)and Sequence Quadratic Programming(SQP)in combination to calibrate the parameters and get the optimal parameter combination in this research.Then,we compare and analyze the simulation result with the other two respectively using initial parameters and parameters obtained by PSO algorithm calibration alone.The result shows that the calibration result of PSO-SQP combined algorithm has the highest accuracy and shows highly consistent with the actual situation,which provides a scientific and effective new idea for parameter calibration of SWMM model,moreover,has practical guidance for flood control and disaster mitigation.
文摘In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines, forming the Geographical WeightedRegression (GWR). Based on classic statistical techniques, GWR model has ascertainsignificance in solving spatial dependence and spatial non-uniform problems, but it hasno impact on the integration of the scale effect. It does not consider the interactionbetween the various factors of the sampling scale observations and the numerous factorsof possible scale effects, so there is a loss of information. Crossing a two-stage analysisof “return of regression” to establish the model of Hierarchical Geographically WeightedRegression (HGWR), the first layer of regression analysis reflects the spatial dependenceof space samples and the second layer of the regression reflects the spatial relationshipsscaling. The combination of both solves the spatial scale effect analysis, spatialdependence and spatial heterogeneity of the combined effects.