夜间短时消防栓测试提升管网压力梯度，显著提高水网模型校准精度。
Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials
- 通过夜间短时消防栓放水制造更大压力差，改善校准条件。
- 相比常规日耗数据校准，误差降低最高达45%。
- 适用于管道过大的老旧水网，适合水务工程人员参考。
水力管网模型的校准是降低不确定性的重要环节。然而，某些管网因管道过大，在日常工况下压力梯度较弱，难以有效校准。本研究提出一种基于夜间短时消防栓试验的校准方法，通过人为增加管网压力梯度来改善校准条件，并将采集数据重采样以匹配小时级用水模式。在某实际水网区域的案例研究中，该方法在统计上显著优于基于日常用水数据的校准方式。实验方法借鉴机器学习交叉验证框架，结合两种先进校准算法，在最优情况下绝对误差降低高达45%。
原文摘要 · Abstract (English)
Calibration is a critical process for reducing uncertainty in Water Distribution Network Hydraulic Models (WDN HM). However, features of certain WDNs, such as oversized pipelines, lead to shallow pressure gradients under normal daily conditions, posing a challenge for effective calibration. This study proposes a calibration methodology using short hydrant trials conducted at night, which increase the pressure gradient in the WDN. The data is resampled to align with hourly consumption patterns. In a unique real-world case study of a WDN zone, we demonstrate the statistically significant superiority of our method compared to calibration based on daily usage. The experimental methodology, inspired by a machine learning cross-validation framework, utilises two state-of-the-art calibration algorithms, achieving a reduction in absolute error of up to 45% in the best scenario.
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