University of Oulu

Hosseini, F. S., Choubin, B., Mosavi, A., Nabipour, N., Shamshirband, S., Darabi, H., & Haghighi, A. T. (2020). Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. Science of The Total Environment, 711, 135161. https://doi.org/10.1016/j.scitotenv.2019.135161

Flash-flood hazard assessment using Ensembles and Bayesian-based machine learning models : application of the simulated annealing feature selection method

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Author: Hosseini, Farzaneh Sajedi1; Choubin, Bahram2; Mosavi, Amir3,4;
Organizations: 1Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran
3School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
4Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary
5Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
6Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
7Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
8Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2019120245245
Language: English
Published: Elsevier, 2019
Publish Date: 2021-11-21
Description:

Abstract

Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the most devastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy= 90−92%, Kappa= 79−84%, Success ratio= 94−96%, Threat score= 80−84%, and Heidke skill score= 79−84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.

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Series: Science of the total environment
ISSN: 0048-9697
ISSN-E: 1879-1026
ISSN-L: 0048-9697
Volume: 711
Article number: 135161
DOI: 10.1016/j.scitotenv.2019.135161
OADOI: https://oadoi.org/10.1016/j.scitotenv.2019.135161
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Subjects:
Copyright information: © 2019 Elsevier B.V. All rights reserved.