基于BP与PSO的河口双边界条件逆向推求方法

Inverse derivation of double boundary conditions for estuaries based on BP and PSO

在入海河道的防洪调度计算过程中,经常会遇到在确定河口计算区间某一断面水位条件下逆向推求双边界(上游流量、下游潮位)条件的情形。针对基于MIKE 11模型的传统试算法逆向推求河口双边界条件时计算效率与搜索效率低的问题,提出采用BP神经网络和PSO算法来改进传统试算法的优化方法。该方法首先通过MIKE 11模型建立河口双边界与某一断面水位的离线数据库,然后利用BP神经网络建立河口双边界与该断面水位的高精度非线性映射关系,最后以该断面的确定水位为优化目标,采用PSO算法逆向推求进而确定双边界条件的映射关系。晋江河口的实例研究表明:该优化方法与传统试算法相比,在保障计算精度的前提下,计算时长约减少至原来的十分之一,大大提高了计算效率。研究成果可为河口地区防洪调度提供参考依据。

In the process of flood control and operation calculation for estuaries,it is often encountered that under the determined water level at a certain section in the research section of the estuary,the double boundaries(upstream flow and downstream tide level)conditions are required to derive in reverse.Aiming at the problems of low computational accuracy and search efficiency of the traditional trial algorithm based on the MIKE 11 model for inverse deducing of estuarine double boundaries conditions,this paper proposed an optimization method using Back Propagation(BP)neural network and Particle Swarm Optimizer(PSO)algorithm to improve the traditional trial algorithm.Specifically,the offline database of the double boundary of the estuary and the water level of acertain section were established by the MIKE 11 model.Subsequently,the BP neural network was used to establish the high-precision non-linear mapping relationship between the double boundaries and the water level of the section.Finally,taking the determined water level of the section as the optimization objective,PSO algorithm was used to reversely calculate and determine the mapping relationship of the double boundaries.A case study of Jinjiang River Estuary showed that compared with the traditional trial algorithm,the optimization method reduced the calculation time to about one-tenth of the original one,and greatly improved the calculation efficiency.The research results can provide reference basis for flood control and operation of Jinjiang River Estuary.