Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials
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arXiv
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| Format: | Preprint |
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2024
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| author | Kołodziej, Katarzyna Cholewa, Michał Głomb, Przemysław Koral, Wojciech Romaszewski, Michał |
| author_facet | Kołodziej, Katarzyna Cholewa, Michał Głomb, Przemysław Koral, Wojciech Romaszewski, Michał |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02772 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials Kołodziej, Katarzyna Cholewa, Michał Głomb, Przemysław Koral, Wojciech Romaszewski, Michał Signal Processing Machine Learning 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. |
| title | Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2410.02772 |