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/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


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 (Electronic)9781509060177
Publication statusPublished - 28 Sept 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings


Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CityRio de Janeiro

Bibliographical note

Funding Information:
Mohammed Khalaf1,2, Abir Jaafar Hussain1, Dhiya Al-Jumeily1, Thar Baker1, Robert Keight1, Paulo Lisboa1, Paul Fergus1, Ala S. Al Kafri1 1Faculty of Engineering and Technology, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK 2 University of Anbar, Ministry of Higher Education and Scientific Research, Bagdad, Al-Rusafa Region, Iraq M.I.Khalaf@2014.ljmu.ac.uk, {a.hussain, d.aljumeily, P.Fergus, T.Baker, P.J.lisboa}@ljmu.ac.uk, R.Keight@2015.ljmu.ac.uk.

Publisher Copyright:
© 2018 IEEE.


  • Accuracy
  • Big data
  • Flood datasets
  • Machine learning approaches
  • Performance evaluations
  • Receiver operating characteristic (ROC)
  • The Area Under Curve (AUC)


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