Sökning: WFRF:(Ansari Moghaddam Alireza)
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Flood susceptibilit...
Flood susceptibility mapping using support vector regression and hyper-parameter optimization
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- Salvati, Aryan (författare)
- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran
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- Nia, Alireza Moghaddam (författare)
- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran
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- Salajegheh, Ali (författare)
- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran
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- Ghaderi, Kayvan (författare)
- Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
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- Asl, Dawood Talebpour (författare)
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
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- Al-Ansari, Nadhir (författare)
- Luleå tekniska universitet,Geoteknologi
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- Solaimani, Feridon (författare)
- Department of Soil Conservation and Watershed Management Research, Khuzestan Agricultural and Natural Resources Research and Education Center, AREEO, Ahvaz, Iran
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- Clague, John J. (författare)
- Department of Earth Sciences, Simon Fraser University, Burnaby, Canada
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(creator_code:org_t)
- John Wiley and Sons Inc, 2023
- 2023
- Engelska.
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Ingår i: Journal of Flood Risk Management. - : John Wiley and Sons Inc. - 1753-318X. ; 16:4
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Floods are both complex and destructive, and in most parts of the world cause injury, death, loss of agricultural land, and social disruption. Flood susceptibility (FS) maps are used by land-use managers and land owners to identify areas that are at risk from flooding and to plan accordingly. This study uses machine learning ensembles to produce objective and reliable FS maps for the Haraz watershed in northern Iran. Specifically, we test the ability of the support vector regression (SVR), together with linear kernel (LK), base classifier (BC), and hyper-parameter optimization (HPO), to identify flood-prone areas in this watershed. We prepared a map of 201 past floods to predict future floods. Of the 201 flood events, 151 (75%) were used for modeling and 50 (25%) were used for validation. Based on the relevant literature and our field survey of the study area, 10 effective factors were selected and prepared for flood zoning. The results show that three of the 10 factors are most important for predicting flood-sensitive areas, specifically and in order of importance, slope, distance to the river and river. Additionally, the SVR-HPO model, with area under the curve values of 0.986 and 0.951 for the training and testing phases, outperformed the other two tested models.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)
Nyckelord
- flood susceptibility
- GIS
- hyper-parameter optimization
- Iran
- linear kernel
- SVR
- Geoteknik
- Soil Mechanics
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