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Article

Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

by 1,2, 3, 4, 4, 4, 5 and 6,7,8,9,*
1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
3
Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
4
Department of Geography, The University of Burdwan, West Bengal 713 104, India
5
Department of Earth and Environment, AHC-5-390, Florida International University, Miami, FL 33199, USA
6
Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
7
School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
8
Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
9
Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3568; https://doi.org/10.3390/rs12213568
Received: 7 September 2020 / Revised: 23 October 2020 / Accepted: 28 October 2020 / Published: 31 October 2020
Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region. View Full-Text
Keywords: flash-flood susceptibility; parallel random forest; regularized random forest; extremely randomized trees (ERT); big data; artificial intelligence; machine learning; natural hazard; hydrological model; data science flash-flood susceptibility; parallel random forest; regularized random forest; extremely randomized trees (ERT); big data; artificial intelligence; machine learning; natural hazard; hydrological model; data science
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MDPI and ACS Style

Band, S.S.; Janizadeh, S.; Chandra Pal, S.; Saha, A.; Chakrabortty, R.; Melesse, A.M.; Mosavi, A. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sens. 2020, 12, 3568. https://doi.org/10.3390/rs12213568

AMA Style

Band SS, Janizadeh S, Chandra Pal S, Saha A, Chakrabortty R, Melesse AM, Mosavi A. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sensing. 2020; 12(21):3568. https://doi.org/10.3390/rs12213568

Chicago/Turabian Style

Band, Shahab S., Saeid Janizadeh, Subodh Chandra Pal, Asish Saha, Rabin Chakrabortty, Assefa M. Melesse, and Amirhosein Mosavi. 2020. "Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms" Remote Sensing 12, no. 21: 3568. https://doi.org/10.3390/rs12213568

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