| 引用本文: | 刘兴坡,程星铁,胡小婷,李永战.基于响应面优化的青龙河流域HSPF模型参数校准方法[J].哈尔滨工业大学学报,2019,51(5):163.DOI:10.11918/j.issn.0367-6234.201806067 |
| LIU Xingpo,CHENG Xingtie,HU Xiaoting,LI Yongzhan.Parameter calibration method of HSPF model for Qinglong River watershed based on response surface optimization[J].Journal of Harbin Institute of Technology,2019,51(5):163.DOI:10.11918/j.issn.0367-6234.201806067 |
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| 基于响应面优化的青龙河流域HSPF模型参数校准方法 |
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刘兴坡1,2,程星铁1,2,胡小婷1,2,李永战3
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(1.上海海事大学 海洋科学与工程学院 ,上海,201306;2.上海海事大学 海洋环境与生态模拟研究中心,上海, 201306;3.河北省桃林口水库管理局,河北 秦皇岛,066400)
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| 摘要: |
| Hydrological Simulation Program-Fortran（HSPF）模型参数多且交互作用复杂,传统参数寻优面临着优化参数不灵敏、优化算法易陷入局部陷阱等问题,影响了优化精度和效率.本文集成青龙河流域、参数抽样、灵敏度分析和参数优化探索新的寻优途径.应用响应面优化软件Design Expert,针对9个HSPF模型参数进行抽样,获得130组参数集,采用多元二次回归模型,建立参数集与纳什效率系数（NSE）的非线性关系,通过等高线和响应面识别最优参数及其密集取值区间.响应面优化参数的NSE平均值、最大值、最小值以及寻优区间缩减率均优于正交极差分析方法；LZSN、INFILT、AGWRC为极灵敏参数,DEEPFR为灵敏参数；LZSN和INFILT、INFILT和AGWRC、INFILT和UZSN、INFILT和IRC的交互作用对结果有显著影响；优化参数的密集取值区间:LZSN[2.0,2.65]；INFILT[0.0,0.475]；AGWRC[0.0,0.885]；DEEPFR[0.1,0.176]；BASETP[0.1,0.120]；AGWETP[0,3,0.120]；CEPSC[0.6,0.244]；UZSN[0.3,1.22]； IRC[0.3,0.63] .响应面方法综合了参数抽样、参数灵敏度分析以及参数优化等3个方面,考虑了参数非线性关系和参数的交互作用,兼顾了优化精度和效率,为青龙河流域HSPF模型参数优化开拓了新途径. |
| 关键词: 青龙河流域 HSPF模型 参数抽样 参数优化 响应面优化法 |
| DOI：10.11918/j.issn.0367-6234.201806067 |
| 分类号:X522 |
| 文献标识码:A |
| 基金项目:城市水资源与水环境国家重点实验室开放课题(ES201104)；国家自然科学基金(51008191) |
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| Parameter calibration method of HSPF model for Qinglong River watershed based on response surface optimization |
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LIU Xingpo1,2,CHENG Xingtie1,2,HU Xiaoting1,2,LI Yongzhan3
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(1.College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China; 2.Center for Marine Environmental and Ecological Modelling, Shanghai Maritime University, Shanghai, 201306, China; 3.Taolinkou Reservoir Administration, Qinhuangdao, 066400 Hebei, China）
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| Abstract: |
| Hydrological Simulation Program-Fortran (HSPF) model has many parameters and complex interactions. The traditional parameter optimization is insensitive to the optimization parameters and the optimization algorithm is easy to trap into local problems, which affects the precision and efficiency of optimization. In this paper, a new optimization approach is explored by integrating Qinglong River watershed, parameter sampling, sensitivity analysis, and parameter optimization. Response surface optimization software Design Expert was applied to sample the parameters of 9 HSPF models, and 130 sets of parameter sets were obtained. Multiple quadratic regression models were used to establish the nonlinear relationship between the parameter sets and the efficiency coefficient of nash-sutcliffe (NSE), and the optimal parameters and their dense value ranges were identified by contour lines and response surface. The NSE mean value, maximum value, and minimum value of the response surface optimization parameters as well as the optimized interval reduction rate were all superior to the orthogonal range analysis method. LZSN, INFILT, and AGWRC were extremely sensitive parameters, while DEEPFR was sensitive parameters. The interactions between LZSN and INFILT, INFILT and AGWRC, INFILT and UZSN, and INFILT and IRC had significant impacts on the results. The dense value range of parameters were optimized as follows: LZSN[2.0,2.65], INFILT[0.0,0.475], AGWRC[0.0,0.885], DEEPFR[0.1,0.176], BASETP[0.1,0.120], AGWETP[0,3,0.120], CEPSC[0.6,0.244], UZSN[0.3,1.22], IRC[0.3,0.63]. The response surface method synthesized three aspects, i.e., parameter sampling, parameter sensitivity analysis, and parameter optimization, which considers the nonlinear relationship of parameters, the interaction of parameters, and the optimization accuracy and efficiency, thus opening up a new way for parameter optimization of HSPF model in Qinglong River watershed. |
| Key words: Qinglong River watershed HSPF model parameters sampling parameter optimization response surface optimization method |
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