ZHANGHaibo,ZHOUYi,GONGYuanyuan,et al.Parameter Calibration of SWMM for Combined Sewer Systems Based on Data Cleaning[J].China Water & Wastewater,2026,42(5):131-136.
基于数据清洗的合流制排水系统SWMM参数率定
- Title:
- Parameter Calibration of SWMM for Combined Sewer Systems Based on Data Cleaning
- 关键词:
- 合流制排水系统; 雨洪管理模型（SWMM）; 数据清洗; 模型率定; 粒子群优化（PSO）算法
- Keywords:
- combined sewer system; storm water management model (SWMM); data cleaning; model calibration; particle swarm optimization (PSO) algorithm
- 摘要:
- 合流制排水系统模拟对城市内涝、合流制溢流（CSO）风险评估等具有重要意义，而雨洪管理模型（SWMM）的参数率定是提高模拟精度的关键环节。为此，针对流量监测数据质量问题，通过数据清洗，识别并修正了旱天和雨天流量的异常值；同时，采用粒子群优化（PSO）算法对SWMM中的敏感性参数进行率定，并结合纳什效率系数（NSE）等指标评估模型精度。以武汉市HXH流域的部分合流制管渠系统为研究案例，结果表明，数据清洗可有效降低异常值对模型率定的干扰；下游边界条件和降雨过程的精细化程度对模拟精度影响显著；PSO算法在SWMM参数率定中表现出高效性，结合分场景动态校准参数可显著提升模型在中高强度降雨事件中的模拟精度（保留场次NSE均大于0.6，洪峰相对误差均控制在±10%以内）。
- Abstract:
- Simulation of combined sewer systems is crucial for assessing urban waterlogging risks and combined sewer overflow (CSO). Accurate parameters calibration of the storm water management model (SWMM) is key to improving simulation reliability. To address quality issues in flow monitoring data, this study applied data cleaning techniques to identify and correct outliers of flow data at dry and wet weather. The particle swarm optimization (PSO) algorithm was employed to calibrate sensitive parameters in SWMM, with model accuracy evaluated using metrics such as the Nash-Sutcliffe efficiency (NSE) coefficient. A case study was conducted on a section of the combined sewer network in the HXH catchment of Wuhan. Results demonstrated that data cleaning effectively reduced interference from outliers during model calibration. Downstream boundary conditions and the temporal refinement of rainfall data significantly influenced simulation accuracy. The PSO algorithm proved efficient for SWMM parameter calibration, and its combination with scenario-based dynamic parameter adjustment notably improved model performance for medium- to high-intensity rainfall events (all calibrated events achieved NSE>0.6 and peak flow relative errors within ±10%).
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