Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh

Zakaria Shams Siam; Rubyat Tasnuva Hasan; Soumik Sarker Anik; Fahima Noor; Mohammed Sarfaraz Gani Adnan; Rashedur M. Rahman

doi:10.1007/978-3-030-79463-7_6

Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh

Zakaria Shams Siam, Rubyat Tasnuva Hasan, Soumik Sarker Anik, Fahima Noor, Mohammed Sarfaraz Gani Adnan, Rashedur M. Rahman^*

^*Corresponding author for this work

Civil And Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

21 Citations (Scopus)

Abstract

Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learning and other numerical models in mapping flood susceptibility. However, no study has been conducted on the flood dataset of Bangladesh using hybridized SVR model to predict flood susceptibility. In the present study, we have collected and modeled the recent flood inundation dataset of Bangladesh in terms of nine flood factors and explored their relative importance rank using the random forest (RF) algorithm. Then, we employed a genetic algorithm (GA) optimized SVR with radial basis function (RBF) kernel (hybridized GA-RBF-SVR) model along with the stand-alone RBF-SVR and multilayer perceptron (MLP) models to predict the flood susceptibility map for the whole country. The result of the hybridized SVR model is very promising to be employed in decision making to deal with the flood forecasting problem in Bangladesh.

Original language	English
Title of host publication	Advances and Trends in Artificial Intelligence. From Theory to Practice
Editors	Hamido Fujita, Ali Selamat, Jerry Chun-Wei Lin, Moonis Ali
Place of Publication	Cham
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	59-71
Number of pages	13
ISBN (Print)	9783030794620
DOIs	https://doi.org/10.1007/978-3-030-79463-7_6
Publication status	Published - 19 Jul 2021
Event	34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021 - Virtual, Online Duration: 26 Jul 2021 → 29 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12799 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021
City	Virtual, Online
Period	26/07/21 → 29/07/21

Funding

This work is supported by the ICT Innovation Fund (2020-21) provided by the ICT division, Ministry of Post, Telecommunication and Information Technology of the People’s Republic of Bangladesh.

Keywords

flood inventory
flood susceptibility mapping
hybridized support vector regression
machine learning
multilayer perceptron
random forest

Access to Document

10.1007/978-3-030-79463-7_6

Cite this

Siam, Z. S., Hasan, R. T., Anik, S. S., Noor, F., Adnan, M. S. G., & Rahman, R. M. (2021). Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh. In H. Fujita, A. Selamat, J. C.-W. Lin, & M. Ali (Eds.), Advances and Trends in Artificial Intelligence. From Theory to Practice (pp. 59-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12799 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-79463-7_6

Siam, Zakaria Shams ; Hasan, Rubyat Tasnuva ; Anik, Soumik Sarker et al. / Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh. Advances and Trends in Artificial Intelligence. From Theory to Practice. editor / Hamido Fujita ; Ali Selamat ; Jerry Chun-Wei Lin ; Moonis Ali. Cham : Springer Science and Business Media Deutschland GmbH, 2021. pp. 59-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c8c80ddc774b42e4a1f0d1726c2eba55,

title = "Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh",

abstract = "Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learning and other numerical models in mapping flood susceptibility. However, no study has been conducted on the flood dataset of Bangladesh using hybridized SVR model to predict flood susceptibility. In the present study, we have collected and modeled the recent flood inundation dataset of Bangladesh in terms of nine flood factors and explored their relative importance rank using the random forest (RF) algorithm. Then, we employed a genetic algorithm (GA) optimized SVR with radial basis function (RBF) kernel (hybridized GA-RBF-SVR) model along with the stand-alone RBF-SVR and multilayer perceptron (MLP) models to predict the flood susceptibility map for the whole country. The result of the hybridized SVR model is very promising to be employed in decision making to deal with the flood forecasting problem in Bangladesh.",

keywords = "flood inventory, flood susceptibility mapping, hybridized support vector regression, machine learning, multilayer perceptron, random forest",

author = "Siam, \{Zakaria Shams\} and Hasan, \{Rubyat Tasnuva\} and Anik, \{Soumik Sarker\} and Fahima Noor and Adnan, \{Mohammed Sarfaraz Gani\} and Rahman, \{Rashedur M.\}",

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booktitle = "Advances and Trends in Artificial Intelligence. From Theory to Practice",

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Siam, ZS, Hasan, RT, Anik, SS, Noor, F, Adnan, MSG & Rahman, RM 2021, Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh. in H Fujita, A Selamat, JC-W Lin & M Ali (eds), Advances and Trends in Artificial Intelligence. From Theory to Practice. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12799 LNAI, Springer Science and Business Media Deutschland GmbH, Cham, pp. 59-71, 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Virtual, Online, 26/07/21. https://doi.org/10.1007/978-3-030-79463-7_6

Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh. / Siam, Zakaria Shams; Hasan, Rubyat Tasnuva; Anik, Soumik Sarker et al.
Advances and Trends in Artificial Intelligence. From Theory to Practice. ed. / Hamido Fujita; Ali Selamat; Jerry Chun-Wei Lin; Moonis Ali. Cham: Springer Science and Business Media Deutschland GmbH, 2021. p. 59-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12799 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh

AU - Siam, Zakaria Shams

AU - Hasan, Rubyat Tasnuva

AU - Anik, Soumik Sarker

AU - Noor, Fahima

AU - Adnan, Mohammed Sarfaraz Gani

AU - Rahman, Rashedur M.

PY - 2021/7/19

Y1 - 2021/7/19

N2 - Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learning and other numerical models in mapping flood susceptibility. However, no study has been conducted on the flood dataset of Bangladesh using hybridized SVR model to predict flood susceptibility. In the present study, we have collected and modeled the recent flood inundation dataset of Bangladesh in terms of nine flood factors and explored their relative importance rank using the random forest (RF) algorithm. Then, we employed a genetic algorithm (GA) optimized SVR with radial basis function (RBF) kernel (hybridized GA-RBF-SVR) model along with the stand-alone RBF-SVR and multilayer perceptron (MLP) models to predict the flood susceptibility map for the whole country. The result of the hybridized SVR model is very promising to be employed in decision making to deal with the flood forecasting problem in Bangladesh.

AB - Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learning and other numerical models in mapping flood susceptibility. However, no study has been conducted on the flood dataset of Bangladesh using hybridized SVR model to predict flood susceptibility. In the present study, we have collected and modeled the recent flood inundation dataset of Bangladesh in terms of nine flood factors and explored their relative importance rank using the random forest (RF) algorithm. Then, we employed a genetic algorithm (GA) optimized SVR with radial basis function (RBF) kernel (hybridized GA-RBF-SVR) model along with the stand-alone RBF-SVR and multilayer perceptron (MLP) models to predict the flood susceptibility map for the whole country. The result of the hybridized SVR model is very promising to be employed in decision making to deal with the flood forecasting problem in Bangladesh.

KW - flood inventory

KW - flood susceptibility mapping

KW - hybridized support vector regression

KW - machine learning

KW - multilayer perceptron

KW - random forest

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DO - 10.1007/978-3-030-79463-7_6

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances and Trends in Artificial Intelligence. From Theory to Practice

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A2 - Selamat, Ali

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PB - Springer Science and Business Media Deutschland GmbH

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Siam ZS, Hasan RT, Anik SS, Noor F, Adnan MSG, Rahman RM. Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh. In Fujita H, Selamat A, Lin JCW, Ali M, editors, Advances and Trends in Artificial Intelligence. From Theory to Practice. Cham: Springer Science and Business Media Deutschland GmbH. 2021. p. 59-71. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-79463-7_6

Study of hybridized support vector regression based flood susceptibility mapping for Bangladesh

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