Principal Component Analysis of Water Pipe Flow Data

被引：4

作者：

Park, S. ^{[1
]}

Jung, S. -Y. ^{[1
]}

机构：

[1] Pusan Natl Univ, Dept Civil & Environm Engn, Busan 609735, South Korea

来源：

16TH WATER DISTRIBUTION SYSTEM ANALYSIS CONFERENCE (WDSA2014): URBAN WATER HYDROINFORMATICS AND STRATEGIC PLANNING | 2014年 / 89卷

关键词：

flow data; outliers; principal component analysis; water distribution system;

D O I：

10.1016/j.proeng.2014.11.204

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this study the Principal Component Analysis (PCA) technique was applied to the night flow data collected from a water distribution system in South Korea. The principal components of the observed flow data were determined, and the scores of the data and the model residual errors were used to calculate the statistics T-2 Hotelling and Distance to Model (DMOD) which represent severe and moderate outliers for the PCA model, respectively. The changes in the dates which have the outliers obtained from the PCA using various analysis periods were analyzed. As a result, the best flow data size for the PCA of a District Metered Area was determined. (C) 2014 The Authors. Published by Elsevier Ltd.

引用

页码：395 / 400

页数：6