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Development of novel hybridized models for urban flood susceptibility mapping

Rahmati, Omid ; Darabi, Hamid ; Panahi, Mahdi ; Kalantari, Zahra ; Naghibi, Seyed Amir LU ; Ferreira, Carla Sofia Santos ; Kornejady, Aiding ; Karimidastenaei, Zahra ; Mohammadi, Farnoush and Stefanidis, Stefanos , et al. (2020) In Scientific Reports 10(1).
Abstract

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency... (More)

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
10
issue
1
article number
12937
publisher
Nature Publishing Group
external identifiers
  • pmid:32737384
  • scopus:85088861143
ISSN
2045-2322
DOI
10.1038/s41598-020-69703-7
language
English
LU publication?
yes
id
7366c5c5-f80c-432f-aae4-4dbabfe014b9
date added to LUP
2020-08-07 11:02:02
date last changed
2020-10-20 03:03:29
@article{7366c5c5-f80c-432f-aae4-4dbabfe014b9,
  abstract     = {<p>Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.</p>},
  author       = {Rahmati, Omid and Darabi, Hamid and Panahi, Mahdi and Kalantari, Zahra and Naghibi, Seyed Amir and Ferreira, Carla Sofia Santos and Kornejady, Aiding and Karimidastenaei, Zahra and Mohammadi, Farnoush and Stefanidis, Stefanos and Tien Bui, Dieu and Haghighi, Ali Torabi},
  issn         = {2045-2322},
  language     = {eng},
  number       = {1},
  publisher    = {Nature Publishing Group},
  series       = {Scientific Reports},
  title        = {Development of novel hybridized models for urban flood susceptibility mapping},
  url          = {http://dx.doi.org/10.1038/s41598-020-69703-7},
  doi          = {10.1038/s41598-020-69703-7},
  volume       = {10},
  year         = {2020},
}