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

Khalaf, M, Hussain, A, Al-Jumeily, D, Shamsa, T.M, Keight, R, Lisboa, P, Alkafri, A and Fergus, P A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. In: IEEE Congress on Evolutionary Computation, 08 July 2018 - 13 July 2018, Brazil. (Accepted)

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Applied Mathematics
Computer Science
Publisher: IEEE Publishing
Date Deposited: 16 Apr 2018 09:33
Last Modified: 08 Jul 2018 00:50
URI: http://researchonline.ljmu.ac.uk/id/eprint/8487

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