University of Oulu

Rahmati, O., Darabi, H., Panahi, M. et al. Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10, 12937 (2020).

Development of novel hybridized models for urban flood susceptibility mapping

Saved in:
Author: Rahmati, Omid1,2; Darabi, Hamid3; Panahi, Mahdi4,5;
Organizations: 1Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
2Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
3Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
4Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon, 34132, Republic of Korea
5Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do, 24341, Republic of Korea
6Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
7Department Water Resources Engineering and Center for Middle Eastern Studies, Lund University, Lund, Sweden
8Research Centre for Natural Resources, Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Agrarian School of Coimbra, Coimbra, Portugal
9Spatial Sciences Innovators, Consulting Engineering Company, Tehran, Iran
10Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Tehran, Iran
11Laboratory of Mountainous Water Management and Control, Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece
12Institute of Research and Development, Duy Tan University, 550000, Da Nang, Viet Nam
13Geographic Information System group, Department of Business and IT, University of South-Eastern Norway, 3800, Bø i Telemark, Norway
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7.9 MB)
Persistent link:
Language: English
Published: Springer Nature, 2020
Publish Date: 2020-12-04


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.

see all

Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 10
Issue: 1
Article number: 12937
DOI: 10.1038/s41598-020-69703-7
Type of Publication: A1 Journal article – refereed
Field of Science: 212 Civil and construction engineering
218 Environmental engineering
Funding: The authors would like to thank the Amol authority for supplying required data (flooded locations and thematic layers), and reports. The Portuguese Science and Technology Foundation supported this research through the Post-doctoral grant SFRH/BPD/120093/2016. We greatly appreciate the assistance of the Editorial Board Member, Dr. Ashraf Dewan, and anonymous reviewers for their constructive comments that helped us to improve the paper. This work was supported by the Finnish Foundation for Technology Promotion, so authors would like to express special thanks to Finnish Foundation for Technology Promotion for the great financial support this project.
Copyright information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit