Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials
This work addresses calibration uncertainty in water delivery networks, particularly for systems with oversized pipelines, offering a domain-specific improvement that is incremental in nature.
The study tackled the challenge of calibrating Water Distribution Network Hydraulic Models (WDN HM) in oversized pipelines with shallow pressure gradients by proposing a method using short hydrant trials at night to increase pressure gradients and resampling data to hourly patterns. In a real-world case study, this method achieved a statistically significant reduction in absolute error of up to 45% compared to daily usage calibration.
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.