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

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  • M. Abdellatif
  • W. Atherton
  • R. Alkhaddar
  • Y. Osman

Abstract

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • M. Abdellatif & W. Atherton & R. Alkhaddar & Y. Osman, 2015. "Flood risk assessment for urban water system in a changing climate using artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 1059-1077, November.
    • Handle: RePEc:spr:nathaz:v:79:y:2015:i:2:p:1059-1077
    DOI: 10.1007/s11069-015-1892-6
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    References listed on IDEAS

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    1. Al-Subaihi, Ali A., 2002. "Variable Selection in Multivariable Regression Using SAS/IML," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i12).
    2. D.I. Smith, 1999. "Urban Flood Damage and Greenhouse Scenarios - The Implications for Policy: An Example from Australia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 4(3), pages 331-342, September.
    3. P. C. D. Milly & R. T. Wetherald & K. A. Dunne & T. L. Delworth, 2002. "Increasing risk of great floods in a changing climate," Nature, Nature, vol. 415(6871), pages 514-517, January.
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    Cited by:

    1. Nanda Khoirunisa & Cheng-Yu Ku & Chih-Yu Liu, 2021. "A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment," IJERPH, MDPI, vol. 18(3), pages 1-20, January.
    2. Chenghao Zhong & Wengao Lou & Chuting Wang, 2022. "Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events," Mathematics, MDPI, vol. 10(18), pages 1-16, September.
    3. Helena M. Ramos & Mohsen Besharat, 2021. "Urban Flood Risk and Economic Viability Analyses of a Smart Sustainable Drainage System," Sustainability, MDPI, vol. 13(24), pages 1-13, December.

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