Adaptation of multiple regression analysis to identify effective factors of water losses in water distribution systems

Dongwoo Jang,Hyoseon Park,Gyewoon Choi

doi:10.1186/s40713-018-0013-6

Abstract

It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.

Highlights

Smart water grids (SWGs) are required for water supply systems for use in water management platforms, which integrates information and communication technology (ICT) into a single water management scheme
The stepwise, one of MRW method is more accurate than other MRW method in the case of reclassifying factors through multiple regression analysis (MRA) among the results of non-revenue water (NRW) estimation using Principle Component Analysis (PCA)-Artificial Neural Network (ANN)
Statistical methods were used for this and NRW was estimated after re-selecting the factors by MRA in the conventional PCA-ANN method

Summary

Introduction

Smart water grids (SWGs) are required for water supply systems for use in water management platforms, which integrates information and communication technology (ICT) into a single water management scheme. Jang (2017, 2018) and Jang et al (2018) suggested that the combination of PCA and ANN is the optimal method for estimating NRW using statistical methods. Combined water balance in the network could be calculated by real measured data but doing so in real water distribution systems should be difficult because of unconstructed DMAs (District Metered Areas) and the design error of water distribution systems. Calculation of NRW ratio in water distribution systems For NRW estimation, governments and institutes around the world are estimating leaks using those occurring in infrastructure. The world produces around 33 billion cubic meters of NRW every year, mostly caused by leak in water supply systems until 2006. NRW analysis can improve water supply system by performing detailed leak analysis when DMA is established after selecting the initial NRW by main parameters of water distribution system analysis

Methodology for NRW estimation

Findings

Conclusions