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    Use of artificial intelligence to improve resilience and preparedness against adverse flood events

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    Authors
    Saravi, Sara
    Kalawsky, Roy
    Joannou, Demetrios
    Rivas Casado, Monica
    Fu, Guangtao
    Meng, Fanlin
    Affiliation
    Loughborough University
    Issue Date
    2019-05-09
    
    Metadata
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    Abstract
    The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.
    Citation
    Saravi, S., Kalawsky, R., Joannou, D., Rivas-Casado, M., Fu, G. and Meng, F., (2019). 'Use of artificial intelligence to improve resilience and preparedness against adverse flood events'. Water, 11(5), pp, 1-16. DOI: 10.3390/w11050973
    Publisher
    MDPI AG
    Journal
    Water
    URI
    http://hdl.handle.net/10545/624221
    DOI
    10.3390/w11050973
    Additional Links
    https://repository.lboro.ac.uk/account/articles/9402362
    https://www.mdpi.com/2073-4441/11/5/973
    Type
    Article
    Language
    en
    ISSN
    2073-4441
    ae974a485f413a2113503eed53cd6c53
    10.3390/w11050973
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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