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The aim of this study is to address the issue of difficulty in evaluating the combined sewer overflow (CSO) pollution effectively, especially for the monitoring of overflow from non-standard thin-plate weirs (NTPWs). In order to construct a discharge calculation mathematical model (DCMM) of NTPWs in an urban combined sewer overflow system (UCSOS), which can be used to construct a real-time monitoring and early warning system, a physical model constructed firstly for obtaining hydraulic characteristic sets under three flow conditions (free, submerged, pressurized flow). Then, the coefficient function expressions are obtained under three flow conditions through genetic algorithm (GA) fitting, and a DCMM of UCSOS is established subsequently. The results indicate that DCMM exhibits high accuracy, with mean relative error (MRE) at 0.0326, root mean square error (RMSE) at 1.569, and Nash–Sutcliffe efficiency (NSE) coefficient at 0.9387. The DCMM can not only evaluate the discharge capacity under different flow conditions, but also be used to realize reliable and convenient discharge monitoring. Although the DCMM constructed in this case is not universal, the modeling method of combining physical models with GA is an effective new approach to evaluate the discharge capacity of NTPW, which also holds value for application in other cities.

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本研究的目的是解决有效评估合流下水道溢流（CSO）污染的困难问题，特别是非标准薄板堰（NTPW）溢流的监测。为了构建城市合流制下水道溢流系统（UCSOS）中NTPW流量计算数学模型（DCMM），用于构建实时监测预警系统，首先构建了获取水力特性的物理模型。设置在三种流动条件下（自由流、浸没流、加压流）。然后，通过遗传算法（GA）拟合得到三种流动条件下的系数函数表达式，并建立UCSOS的DCMM。结果表明，DCMM 具有较高的准确度，平均相对误差 (MRE) 为 0.0326，均方根误差 (RMSE) 为 1.569，纳什-萨特克利夫效率 (NSE) 系数为 0.9387。DCMM不仅可以评估不同流量条件下的排放能力，还可以用于实现可靠、便捷的排放监测。尽管本案例构建的DCMM不具有通用性，但物理模型与遗传算法相结合的建模方法是评价NTPW放电能力的一种有效的新方法，在其他城市也具有应用价值。