Title: Exploration and application of the value of big data based on data-driven techniques for the hydraulic internet of things
Authors: Qiang Yue; Fusheng Liu; Changqing Song; Jing Liang; Yanmin Liu; Guangsheng Cao
Addresses: College of Water Conservancy and Civil Engineering, Shandong Agricultural University, Taian, China ' College of Water Conservancy and Civil Engineering, Shandong Agricultural University, Taian, China ' Agricultural Big Data Center, Shandong Agricultural University, Taian, China ' College of Information Engineering, Qingdao Binhai University, Qingdao, China ' Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada ' College of Information Engineering, Qingdao Binhai University, Qingdao, China
Abstract: The use of big data technology to screen the massive amounts of hydraulic engineering data in the internet of things is important for its efficient application. This research applies big data methodology to water management to solve numerous problems, such as the demand diversification of related interest groups, overall water difficulties and other problems that arise in hydraulic engineering. A historical database that contains a large amount of data and feedback information is used to design an early-warning health model for a reservoir using big data methods and based on the C5.0 decision-tree algorithm. The health status of Dingdong reservoir is forecast using the model as a case study. The results show that the reservoir is in a healthy state corresponding to no warning level. The early-warning health model is feasible and effective for utilising abundant case resources, and could be used widely in reservoir health management. The results obtained in this paper are beneficial to the sustainable development and scientific management of reservoirs.
Keywords: big data; early health warning; water resources data; internet of things.
International Journal of Embedded Systems, 2020 Vol.12 No.1, pp.106 - 115
Received: 02 Dec 2017
Accepted: 31 Mar 2018
Published online: 24 Feb 2020 *