A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Thar Baker, Robert Keight, Paulo Lisboa, Paul Fergus, Ala S. Al Kafri

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide-scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.
    Original languageEnglish
    Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781509060177
    DOIs
    Publication statusPublished - 28 Sep 2018
    Event2018 IEEE Congress on Evolutionary Computation - Rio de Janeiro, Brazil
    Duration: 8 Jul 201813 Jul 2018

    Publication series

    Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

    Conference

    Conference2018 IEEE Congress on Evolutionary Computation
    CountryBrazil
    CityRio de Janeiro
    Period8/07/1813/07/18

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  • Cite this

    Khalaf, M., Hussain, A. J., Al-Jumeily, D., Baker, T., Keight, R., Lisboa, P., Fergus, P., & Al Kafri, A. S. (2018). A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477904