Estimation of Non-Revenue Water Ratio Using MRA and ANN in Water Distribution Networks

被引:16
作者
Jang, Dongwoo [1 ]
Choi, Gyewoon [1 ]
机构
[1] Incheon Natl Univ, Dept Civil & Environm Engn, Incheon 22012, South Korea
关键词
non-revenue water; multiple regression analysis; artificial neural network; water distribution network; REHABILITATION; LOSSES;
D O I
10.3390/w10010002
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The non-revenue water (NRW) ratio in water distribution networks is the ratio of losses from unbilled authorized consumption and apparent and real losses to the total water supply. NRW is an important parameter for prioritizing the improvement of a water distribution system and identifying the influencing parameters. Though the method using multiple regression analysis (MRA) is a statistical analysis method for estimating the NRW ratio using the main parameters of a water distribution system, it has disadvantages in that the accuracy is low compared to the measured NRW ratio. In this study, an artificial neural network (ANN) was applied to estimate the NRW ratio to improve assessment accuracy and suggest an efficient methodology to identify related parameters of the NRW ratio. When using an ANN with the optimal number of neurons, the accuracy of estimation was higher than that of conventional statistical methods, as with MRA.
引用
收藏
页数:13
相关论文
共 28 条
[1]  
Alegre H., 2000, Performance Indicators for Water Supply Services. IWA Manual of Best Practice
[2]  
[Anonymous], 2014, THESIS CHUNGBUK NATL
[3]  
[Anonymous], 1994, Neural networks: a comprehensive foundation
[4]  
Chung JaeJin, 2012, [The Journal of Korean Policy Studies, 한국정책연구], V12, P139
[5]  
Engelhardt MO., 2000, Urban Water, V2, P153, DOI [10.1016/S1462-0758(00)00053-4, DOI 10.1016/S1462-0758(00)00053-4]
[6]  
Frauendorfer R., 2010, ISSUES CHALLENGES RE
[7]  
Gwak J.M., 2013, RES STAT ANAL INFORM
[8]   Water network rehabilitation with structured messy genetic algorithm [J].
Halhal, D ;
Walters, GA ;
Ouazar, D ;
Savic, DA .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 1997, 123 (03) :137-146
[9]  
Heaton J., 2005, INTRO NEURAL NETWORK
[10]  
Jang D. W., 2017, THESIS