YANGChen-chen,FANChong,LIUQi,et al.Investigation and Implementation of SWMM Parameters Optimization Method in Regions without Pipeline Flow Data[J].China Water & Wastewater,2024,40(23):129-136.
无管流数据地区SWMM参数优化方法研究及应用
- Title:
- Investigation and Implementation of SWMM Parameters Optimization Method in Regions without Pipeline Flow Data
- 摘要:
- 城市雨洪模型的精确模拟是洪涝快速预报和预警的基础，而参数直接影响模型的准确性和可靠性，但目前我国现有城市雨水管网实测流量数据匮乏，给城市雨洪模型参数自动优化带来了挑战。为此，提出了RCNOA算法，以解决无管道流量数据条件下的SWMM模型参数自动优化问题，并以长沙市岳麓区东侧为典型研究区域评估其适用性。首先，采用敏感性分析方法确定了包含7个参数的优化集合；然后，利用RCNOA算法进行参数率定，确定N-Imperv、N-Perv、S-Imperv、MaxRate、MinRate、DryTime和Roughness的数值分别为0.045、0.113、1.468 mm、94.819 mm/h、14.585 mm/h、12.527 d和0.016；基于率定后的参数值，在不同降雨重现期下进行模拟，得到相应的径流系数，求得变异系数均在±5%之内，表明校准参数能满足SWMM模型对研究区域径流的模拟。该研究提出的方法可以满足无管道流量数据地区城市雨洪模型参数自动优化要求，有助于城市雨洪模型的可持续性发展，对今后城市应对暴雨洪涝事件、制定内涝防治决策具有重要指导意义。
- Abstract:
- The precise simulation of urban stormwater models serves as the foundation for rapid flood prediction and early warning. The model parameters significantly influence both accuracy and reliability. However, the scarcity of measured discharge data from urban stormwater pipe network in China poses substantial challenges to the automated optimization of these model parameters. In this study, the RCNOA algorithm was proposed to address the challenge of automatic parameter optimization for the SWMM model in the absence of pipeline flow data, and its applicability was assessed in the eastern region of Yuelu District in Changsha City. Initially, sensitivity analysis method was employed to identify the optimal set of seven parameters. Subsequently, the RCNOA algorithm was employed for parameter calibration. The parameters of N-Imperv, N-Perv, S-Imperv, MaxRate, MinRate, DryTime and Roughness were established at values of 0.045, 0.113, 1.468 mm, 94.819 mm/h, 14.585 mm/h, 12.527 d, and 0.016 respectively. Utilizing the calibrated parameter values, the corresponding runoff coefficients were simulated across various rainfall recurrence intervals, with variation coefficients consistently remaining within ±5%. This indicated that the calibration parameters were adequate for the SWMM model to effectively simulate runoff in the study area. The methodology proposed in this study will effectively address the automatic parameter optimization requirements of urban stormwater models in regions lacking pipeline flow data, thereby contributing to the sustainable development of these models. Furthermore, it has important guiding significance for cities to deal with rainstorm and flood events and make waterlogging prevention decisions in the future.
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