基于FCLPSO的水量水质模型参数反演方法研究

朱沈涛; 杨帆; 柳杨; 范子武; 乌景秀; 李子祥

doi:10.13928/j.cnki.wrahe.2025.07.005

水利水电技术（中英文） ›› 2025, Vol. 56 ›› Issue (7) : 54 -66. DOI: 10.13928/j.cnki.wrahe.2025.07.005

水环境治理与生态修复专栏

基于FCLPSO的水量水质模型参数反演方法研究

朱沈涛 ¹^,² ,
杨帆 ¹^,² ,
柳杨 ¹^,² ,
范子武 ¹^,² ,
乌景秀 ¹^,² ,
李子祥 ¹^,²

作者信息 +

Research on parameter inversion methods for water quantity and quality models based on FCLPSO

Shentao ZHU ¹^,² ,
Fan YANG ¹^,² ,
Yang LIU ¹^,² ,
Ziwu FAN ¹^,² ,
Jingxiu WU ¹^,² ,
Zixiang LI ¹^,²

Author information +

文章历史 +

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摘要

【目的】复杂河网水量水质模型中参数多、维数高，模型参数反演难度大，优化目标函数选取、单参数和多参数不同反演方式等对参数反演精度影响需开展深入分析。【方法】提出基于快速综合粒子群优化算法(Fast Comprehensive Learning Particle Swarm Optimization, FCLPSO)的水量水质模型参数反演方法，设计参数反演数值试验，采用LH-OAT全局敏感性分析方法对7种模型性能评价指标进行目标函数优选，并分析模型单参数和多参数反演结果并分析不同反演方式的差异性。【结果】结果显示：NSE^*作为目标函数敏感度最高；不同类型参数均具有较高精度，单参数反演平均相对误差(MRE)为5.2%、变差系数(CV)为7.2%,多参数反演结果MRE为13.5%、CV为14%;多参数反演中水动力指标反演结果优于水质指标反演结果，多参数“分层反演”方式优于“同时反演”方式。【结论】结果表明：该模型参数反演方法具有较高的精度，有助于提升复杂河网水量水质模型参数估计时效性与准确性，为复杂河网数值模拟精度的提升提供了技术支撑。

Abstract

[Objective] Complex river network water quantity and quality models involve numerous parameters and high dimensionality, making parameter inversion challenging. An in-depth analysis is required to investigate how the selection of optimization objective functions and different single-parameter and multi-parameter inversion method affect the accuracy of parameter inversion. [Methods] A parameter inversion method for water quantity and quality models was proposed based on the Fast Comprehensive Learning Particle Swarm Optimization(FCLPSO). Numerical experiments for parameter inversion were designed, and the LH-OAT global sensitivity analysis method was used to optimize the objective function for seven model performance evaluation indicators. Furthermore, the inversion result using single-parameter and multi-parameter inversion method were analyzed, and the differences between different inversion method were examined. [Results] The result showed that NSE^* had the highest sensitivity as the objective function. Parameters of different types achieved high accuracy, with the single-parameter inversion having a mean relative error(MRE) of 5.2% and a coefficient of variation(CV) of 7.2%. The multi-parameter inversion result had an MRE of 13.5% and a CV of 14%. In the multi-parameter inversion, the inversion result of hydrodynamic parameters were better than those of water quality parameters, and the multi-parameter “layered inversion”method outperformed the “simultaneous inversion” method. [Conclusion] The result indicate that the proposed model parameter inversion method achieves high accuracy. It can help improve the timeliness and accuracy of parameter estimation for complex river network water quantity and quality models, providing technical support for improving the accuracy of numerical simulation of complex river networks.