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

doi:10.1109/CEC.2018.8477904

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

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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 language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477904
Publication status	Published - 28 Sept 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

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.

Keywords

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

Access to Document

10.1109/CEC.2018.8477904

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 Article 8477904 (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477904

Khalaf, Mohammed ; Hussain, Abir Jaafar ; Al-Jumeily, Dhiya et al. / A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings).

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title = "A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction",

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.",

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

author = "Mohammed Khalaf and Hussain, {Abir Jaafar} and Dhiya Al-Jumeily and Thar Baker and Robert Keight and Paulo Lisboa and Paul Fergus and {Al Kafri}, {Ala S.}",

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: {\textcopyright} 2018 IEEE.; 2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

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Khalaf, M, Hussain, AJ, Al-Jumeily, D, Baker, T, Keight, R, Lisboa, P, Fergus, P & Al Kafri, AS 2018, A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings., 8477904, 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/CEC.2018.8477904

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. / Khalaf, Mohammed; Hussain, Abir Jaafar; Al-Jumeily, Dhiya et al.
2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477904 (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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AU - Khalaf, Mohammed

AU - Hussain, Abir Jaafar

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AU - Lisboa, Paulo

AU - Fergus, Paul

AU - Al Kafri, Ala S.

N1 - 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.

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N2 - 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.

AB - 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.

KW - Accuracy

KW - Big data

KW - Flood datasets

KW - Machine learning approaches

KW - Performance evaluations

KW - Receiver operating characteristic (ROC)

KW - The Area Under Curve (AUC)

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M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85056288822

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BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Khalaf M, Hussain AJ, Al-Jumeily D, Baker T, Keight R, Lisboa P et al. A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8477904. (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings). doi: 10.1109/CEC.2018.8477904

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

Abstract

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Bibliographical note

Keywords

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