Effect of the leakage location pattern on the speed of recovery in water supply networks

Author:

Nugroho W,Iriawan N

Abstract

Abstract The International Water Association states that Non-Revenue Water is an efficient indicator of the performance of a water company that has been implemented internationally. The volume of water loss expressed as Non-Revenue Water at the global level shows a high number. Every year more than 32 billion m3 of treated water is lost due to leakage from the distribution network. There are four pillars in the network leakage management strategy include management of pressure, speed of recovery, active leakage control, and asset management. These components influence how leakage is managed and affect the volume and value of economic leakage - in a distribution network of water companies. The current annual volume of physical losses tends to increase along with the expansion service area of the distribution network. The rate of increase can be inhibited by the right strategy combining the four components into an effective leakage management strategy. This study analyzes the distribution pattern of leakage points in water distribution pipelines in Malang City, and how much they affect the speed of recovery. The data in this study consist of 5449 pipeline leakage points which include spatial and attributes data. The method used in this study is the Spatial Point Process. The results of the analysis show that the distribution pattern of leakage is an inhomogeneous pattern. It is found that the parameters of the model -with Euclidean distance- the leakage pattern affects the occurrence of leakage and the Geographical Weighted Regression model -with fixed Gaussian Kernel Weight Function with Manhattan distance- the leakage pattern affects the speed of recovery.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference10 articles.

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1. An Automated Geographical Information System-Based Spatial Machine Learning Method for Leak Detection in Water Distribution Networks (WDNs) Using Monitoring Sensors;Applied Sciences;2024-07-04

2. A spatial data-driven decision analysis of pipe failure management in water supply system;PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research;2023

3. Determining physical and operational factors influencing pipeline leakage location pattern in water distribution networks using spatial poisson point process;IOP Conference Series: Materials Science and Engineering;2021-03-01

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