Flood risk assessment for urban water system in a changing climate using artificial neural network

Abdellatif, Mawada E., Atherton, William, Alkhaddar, Rafid and Osman, Yassin Z. ORCID: 0000-0003-1121-6598 (2015) Flood risk assessment for urban water system in a changing climate using artificial neural network. Natural Hazards, 79 (2). pp. 1059-1077. ISSN 1573-0840

osman Paper-6.pdf

Download (1MB) | Preview
Official URL: https://link.springer.com/journal/11069


Changes in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Network, Climate Change, Combined sewer system, Downscaling, Flooding
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: School of Engineering > Civil Engineering
Depositing User: Sarah Taylor
Date Deposited: 28 Mar 2018 09:40
Last Modified: 28 Mar 2018 09:40
URI: http://ubir.bolton.ac.uk/id/eprint/1652

Actions (login required)

Edit Item Edit Item


Downloads per month over past year

View more statistics