The simulation of a one-dimensional river network needs to solve the Saint-Venant equations,in which the variable parameters normally have a significant influence on the model accuracy.A Trial-and-Error approach is a ...The simulation of a one-dimensional river network needs to solve the Saint-Venant equations,in which the variable parameters normally have a significant influence on the model accuracy.A Trial-and-Error approach is a most commonly adopted method of parameter calibration,however,this method is time-consuming and requires experience to select the appropriate values of parameter.Consequently,simulated results obtained via this method usually differ between practitioners.This article combines a hydrodynamic model with an intelligent model originated from the Genetic Algorithm（GA） technique,in order to provide an intelligent simulation method that can optimize the parameters automatically.Compared with current approaches,the method presented in this article is simpler,its dependence on field data is lower,and the model accuracy is higher.When the optimized parameters are taken into the hydrodynamic numerical model,a good agreement is attained between the simulated results and the field data.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 50879019, 50879020)the Key Science Project of Guangdong Traffic Department (Grant No.2008-19)
文摘The simulation of a one-dimensional river network needs to solve the Saint-Venant equations,in which the variable parameters normally have a significant influence on the model accuracy.A Trial-and-Error approach is a most commonly adopted method of parameter calibration,however,this method is time-consuming and requires experience to select the appropriate values of parameter.Consequently,simulated results obtained via this method usually differ between practitioners.This article combines a hydrodynamic model with an intelligent model originated from the Genetic Algorithm（GA） technique,in order to provide an intelligent simulation method that can optimize the parameters automatically.Compared with current approaches,the method presented in this article is simpler,its dependence on field data is lower,and the model accuracy is higher.When the optimized parameters are taken into the hydrodynamic numerical model,a good agreement is attained between the simulated results and the field data.
