干旱区生态环境知识资源中心(Arid): Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models

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DOI	10.1016/j.jhydrol.2022.128001
	Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models
	Panahi, Mahdi; Rahmati, Omid; Kalantari, Zahra; Darabi, Hamid; Rezaie, Fatemeh; Moghaddam, Davoud Davoudi; Ferreira, Carla Sofia Santos; Foody, Giles; Aliramaee, Ramyar; Bateni, Sayed M.; Lee, Chang-Wook; Lee, Saro
通讯作者	Lee, CW
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	611
英文摘要	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.
英文关键词	Flooding Natural disasters Spatial prediction Remote sensing
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000811872900003
WOS关键词	IMPERIALIST COMPETITIVE ALGORITHM ; GREY WOLF OPTIMIZER ; DIFFERENTIAL EVOLUTION ; WATER-RESOURCES ; KAROON RIVER ; TIME-SERIES ; CLASSIFICATION ; ENSEMBLE ; PREDICTION ; VULNERABILITY
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393496
推荐引用方式 GB/T 7714	Panahi, Mahdi,Rahmati, Omid,Kalantari, Zahra,et al. Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models[J],2022,611.
APA	Panahi, Mahdi.,Rahmati, Omid.,Kalantari, Zahra.,Darabi, Hamid.,Rezaie, Fatemeh.,...&Lee, Saro.(2022).Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models.JOURNAL OF HYDROLOGY,611.
MLA	Panahi, Mahdi,et al."Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models".JOURNAL OF HYDROLOGY 611(2022).