基于改进麻雀搜索算法的水质模型多参数反演  

Multi-Parameter Inversion of Water Quality Model based on an Improved Sparrow Search Algorithm

作　　者：彭杨[1] 杨德铭 罗诗琦 张志鸿[1] PENG Yang;YANG De-ming;LUO Shi-qi;ZHANG Zhi-hong(School of Water Conservancy and Hydropower Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学水利与水电工程学院,北京102206

出　　处：《中国农村水利水电》2024年第7期102-109,116,共9页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(51679088);国家重点研发计划项目(2016YFC0402308)。

摘　　要：水质模型参数取值对模型的模拟精度影响很大,为提高BOD-DO水质模型参数反演精度,首先在DobbinsCamp BOD-DO水质模型的基础上,以BOD和DO浓度计算值与实测值之差的加权平方和最小为目标函数,构建了Dobbins-Camp BOD-DO水质多参数反演模型;然后针对麻雀搜索算法(Sparrow Search Algorithm,SSA)求解精度低、稳定性不足和易陷入局部最优等问题,引入Sine混沌映射和对立学习、转移概率以及差分变异3个策略,分别从提高初始种群多样性、扩大搜索空间以及增强种群跳出局部最优的能力三方面对SSA算法进行改进,提出了一种多策略改进的麻雀搜索算法(Multi-strategy Improved Sparrow Search Algorithm,MISSA),并将其应用于Dobbins-Camp BOD-DO水质多参数反演模型的求解;最后通过数值实验将得到的反演结果与SSA算法、模拟退火算法、粒子群算法、遗传算法四种优化算法进行对比,并探讨了参数初值选取和观测噪声水平对反演结果的影响。结果表明:MISSA算法的计算性能明显优于对照组中的4种算法,且能显著降低初值选取对BOD-DO水质模型参数反演结果的影响,当观测数据的噪声水平不超过5%时,MISSA算法可有效提高反演结果的稳定性。该结果验证了MISSA算法在反演Dobbins-Camp BOD-DO水质模型参数的有效性,为水质模型参数求解提供有益参考。The accuracy of water quality simulation in the BOD-DO model is significantly influenced by the values of its parameters.In order to enhance the inversion accuracy of the BOD-DO water quality model parameters,this study firstly constructs a multi-parameter inversion model based on the Dobbins-Camp BOD-DO water quality model.The objective of the proposed model is to simultaneously determine the op⁃timal values of the oxygen consuming coefficient(K1),aeration coefficient(K2)and BOD settling and rising coefficient(K3)in the Dobbins-Camp BOD-DO water quality model by minimizing the discrepancies between the calculated and measured values of BOD and DO concentra⁃tions.Then,to overcome the challenges associated with the Sparrow Search Algorithm(SSA),such as low solution accuracy,insufficient stability,and easily falling into local optima in the SSA,three strategies are introduced:a combination of Sine chaotic mapping with opposi⁃tion-based learning,transition probability,and differential variation.These strategies are designed to improve the diversity of the initial pop⁃ulation,expand the search space,and enhance the population′s ability to escape local optima respectively.Based on these improvements,a Multi-strategy Improved Sparrow Search Algorithm(MISSA)is proposed for solving the established Dobbins-Camp BOD-DO water quality multi-parameter inversion model.Finally,numerical experiments are conducted to evaluate the effectiveness of the MISSA by comparing the inversion results obtained from MISSA with four intelligent optimization algorithms:Sparrow Search Algorithm,Simulated Annealing Algo⁃rithm,Particle Swarm Optimization,and Genetic Algorithm,.Additionally,the impacts of observation noise and initial values on the inver⁃sion results are discussed.The results show that the computational performance of the MISSA algorithm significantly surpasses that of the four algorithms in the control group.Moreover,it can effectively reduce the influence of initial value selection on the inversion results of the B

关 键 词：BOD-DO水质模型 参数反演 多策略改进的麻雀搜索算法 初值选取 观测噪声水平 

分 类 号：TV11[水利工程—水文学及水资源] X522[环境科学与工程—环境工程]