Title: A novel flood defense decision support system for smart urban management based on classification and regression tree

Authors: Guanlin Chen; Taimeng Yang; Rui Huang; Zhuoyue Zhu

Addresses: School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China; College of Computer Science, Zhejiang University, Hangzhou, 310027, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China; College of Computer Science, Zhejiang University, Hangzhou, 310027, China ' E-government Office of Hangzhou Municipal People's Government, Hangzhou, 310020, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China; College of Computer Science, Zhejiang University, Hangzhou, 310027, China

Abstract: With the development of the internet of things technology and awareness technology, all kinds of big data in the city have started to emerge. Under the background in internet plus era, using big data to effectively forecast urban flood disaster, formulating the flood control and disaster mitigation countermeasures in time, is an important subject of urban flood control and research. In this paper, a novel flood defense decision support system (NFDDSS) is proposed. Using historical hydrology data in Hangzhou, this paper proposed a comprehensive consideration of time correlation and spatial correlation of water level prediction model based on classification and regression tree. This model can predict the water level in one to six hours effectively. With this system, supervisors can get timely and effective guidance of flood control and disaster mitigation when the flood season comes.

Keywords: flood defense; water level prediction; classification and regression tree; CART; decision support system; big data.

DOI: 10.1504/IJSN.2018.095150

International Journal of Security and Networks, 2018 Vol.13 No.4, pp.245 - 251

Received: 25 Jan 2018
Accepted: 25 Jan 2018

Published online: 01 Oct 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article