Urban waterlogging monitoring and early warning based on video images
by Fengchang Xue; Juan Tian; Xiaoyi Song; Yan Yan
International Journal of Embedded Systems (IJES), Vol. 13, No. 4, 2020

Abstract: Urban flood disaster causes serious loss to urban residents. Timely access to urban waterlogging conditions has great significance for disaster prevention and disaster relief. Owing to the time resolution limitation of data, the traditional monitoring of urban flood disasters using remote sensing imagery cannot realise real-time automatic monitoring and continuous monitoring of key disaster areas. This paper selects road monitoring video, uses image difference operation and support vector machine (SVM) algorithm to identify the waterlogging area, and uses the region growing method to extract the waterlogging area range. The research results show that this method can be used for continuous monitoring and early warning of urban waterlogging in real time.

Online publication date: Tue, 27-Oct-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com