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Large-scale dynamic...
Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models
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- Panahi, Mahdi (författare)
- Kangwon Natl Univ, Div Sci Educ, Gangwon-do, Chunchon 24341, South Korea.
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- Rahmati, Omid (författare)
- AREEO, Kurdistan Agr & Nat Resources Res & Educ Ctr, Soil Conservat & Watershed Management Res Dept, Sanandaj, Iran.
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- Kalantari, Zahra (författare)
- Stockholms universitet,KTH,Hållbar utveckling, miljövetenskap och teknik,Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, Stockholm, Sweden.;Navarino Environm Observ, Costa Navarino, Messenia 24001, Greece.,Institutionen för naturgeografi,Navarino Environmental Observatory, Greece; KTH Royal Institute of Technology, Sweden
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- Darabi, Hamid (författare)
- Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland.
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- Rezaie, Fatemeh (författare)
- Korea Inst Geosci & Mineral Resources KIGAM, Geosci Data Ctr, 124 Gwahak Ro, Daejeon 34132, South Korea.;Korea Univ Sci & Technol, Dept Geophys Explorat, 217 Gajeong Ro,Yuseong Gu, Daejeon 34113, South Korea.
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- Moghaddam, Davoud Davoudi (författare)
- Lorestan Univ, Agr & Nat Resources Fac, Dept Watershed Management, Khorramabad, Iran.
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- Santos Ferreira, Carla Sofia (författare)
- Stockholms universitet,Institutionen för naturgeografi,Navarino Environmental Observatory, Greece; Agrarian School of Coimbra, Portugal,Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, Stockholm, Sweden.;Navarino Environm Observ, Costa Navarino, Messenia 24001, Greece.;Polytech Inst Coimbra, Res Ctr Nat Resources, Agrarian Sch Coimbra, Environm & Soc CERNAS, Coimbra, Portugal.
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- Foody, Giles (författare)
- Univ Nottingham, Sch Geog, Sir Cl Granger Bldg,Univ Pk, Nottingham NG72RD, Notts, England.
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- Aliramaee, Ramyar (författare)
- Fac Nat Resources Tarbiat Modares Univ, Dept Watershed Management Engn, Nur, Iran.
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- Bateni, Sayed M. (författare)
- Univ Hawaii Manoa, Water Resources Res Ctr, Dept Civil & Environm Engn, Honolulu, HI USA.
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- Lee, Chang-Wook (författare)
- Kangwon Natl Univ, Div Sci Educ, Gangwon-do, Chunchon 24341, South Korea.
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- Lee, Saro (författare)
- Korea Inst Geosci & Mineral Resources KIGAM, Geosci Data Ctr, 124 Gwahak Ro, Daejeon 34132, South Korea.;Korea Univ Sci & Technol, Dept Geophys Explorat, 217 Gajeong Ro,Yuseong Gu, Daejeon 34113, South Korea.
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Kangwon Natl Univ, Div Sci Educ, Gangwon-do, Chunchon 24341, South Korea AREEO, Kurdistan Agr & Nat Resources Res & Educ Ctr, Soil Conservat & Watershed Management Res Dept, Sanandaj, Iran. (creator_code:org_t)
- Elsevier BV, 2022
- 2022
- Engelska.
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 611, s. 128001-
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid machine-learning models for flood mapping in the Ahvaz region, Iran, based on SAR data. Each hybrid model consists of a support vector machine (SVM) algorithm coupled with one of the following metaheuristic optimization procedures: grey wolf optimization (GWO), differential evolution, and the imperialist competitive algorithm. Sentinel-1 acquired SAR images before and during flooding between 20 March and 26 May of 2019. The goodness-of-fit level and predictive capability of each model were scrutinized using overall accuracy, producer accuracy, and user accuracy. The SVM-GWO approach yielded the highest accuracy with overall accuracies of 96.07% and 93.39% in the training and validation steps, respectively. Furthermore, this hybrid model provided the most accurate classification of water-inundation class based on producer accuracy (96.67%) and user accuracy (95.05%). The results highlight that wetland is the last land-use/land-cover type to return to normal conditions due to the many previously dry oxbow lakes that could trap water for a long time. Furthermore, the nine most suitable sites for flood-protection structures (e.g., embankments and levees) were identified based on floodwater distribution analysis. This work describes a robust, data-parsimonious approach that will benefit flood mitigation studies seeking to identify the most suitable locations for embankments based on spatio-temporal flood dynamics.
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)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Nyckelord
- Flooding
- Natural disasters
- Spatial prediction
- Remote sensing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Panahi, Mahdi
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Rahmati, Omid
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Kalantari, Zahra
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Darabi, Hamid
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Rezaie, Fatemeh
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Moghaddam, Davou ...
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visa fler...
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Santos Ferreira, ...
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Foody, Giles
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Aliramaee, Ramya ...
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Bateni, Sayed M.
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Lee, Chang-Wook
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Lee, Saro
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Geovetenskap och ...
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och Oceanografi hydr ...
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Datorseende och ...
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Samhällsbyggnads ...
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- NATURVETENSKAP
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NATURVETENSKAP
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och Geovetenskap och ...
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Journal of Hydro ...
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Kungliga Tekniska Högskolan
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Stockholms universitet