干旱区生态环境知识资源中心(Arid): DEM resolution effects on machine learning performance for flood probability mapping

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DOI	10.1016/j.jher.2021.10.002
	DEM resolution effects on machine learning performance for flood probability mapping
	Avand, Mohammadtaghi; Kuriqi, Alban; Khazaei, Majid; Ghorbanzadeh, Omid
通讯作者	Ghorbanzadeh, O (corresponding author)，Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria.
来源期刊	JOURNAL OF HYDRO-ENVIRONMENT RESEARCH
ISSN	1570-6443
EISSN	1876-4444
出版年	2022
卷号	40
英文摘要	Floods are among the devastating natural disasters that occurred very frequently in arid regions during the last decades. Accurate assessment of the flood susceptibility mapping is crucial in sustainable development. It helps respective authorities to prevent as much as possible their irreversible consequences. The Digital Elevation Model (DEM) spatial resolution is one of the most crucial base layer factors for modeling Flood Probability Maps (FPMs). Therefore, the main objective of this study was to assess the influence of the spatial resolution of the DEMs 12.5 m (ALOS PALSAR) and 30 m (ASTER) on the accuracy of flood probability prediction using three machine learning models (MLMs), including Random Forest (RF), Artificial Neural Network (ANN), and Generalized Linear Model (GLM). This study selected 14 causative factors in the flood as independent variables, and 220 flood locations were selected as dependent variables. Dependent variables were divided into training (70%) and validation (30%) for flood susceptibility modeling. The Receiver Operating Characteristic Curve (ROC), Kappa index, accuracy, and other statistical criteria were used to evaluate the models' accuracy. The results showed that resolving the DEM alone cannot significantly affect the accuracy of flood probability prediction regardless of the applied MLM and independently of the statistical model used to assess the performance accuracy. In contrast, the factors such as altitude, precipitation, and distance from the river have a considerable impact on floods in this region. Also, the evaluation results of the models showed that the RF (AUC(12.5,30m) = 0.983, 0.975) model is more accurate in preparing the FPM than the ANN (AUC(12.5,30m) = 0.949, 0.93) and GLM (AUC(12.5,30m) = 0.965, 0.949) models. This study's solution-oriented findings might help water managers and decision-makers to make the most effective adaptation and mitigation measures against potential flooding.
英文关键词	Heterogeneous data Flood modelling Random forest (RF) Artificial neural network (ANN) Generalized linear model (GLM)
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000747870400001
WOS关键词	FUZZY INFERENCE SYSTEM ; DIFFERENCE WATER INDEX ; WEIGHTS-OF-EVIDENCE ; SUSCEPTIBILITY ASSESSMENT ; LANDSLIDE SUSCEPTIBILITY ; BIVARIATE STATISTICS ; SPATIAL-RESOLUTION ; GENETIC ALGORITHM ; FREQUENCY RATIO ; MODELS
WOS类目	Engineering, Civil ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376957
作者单位	[Avand, Mohammadtaghi] AREEO, Dept Forests Rangelands & Watershed Management En, Kohgiluyeh & Boy Erahmad Agr & Nat Resources Res, Yasuj, Iran; [Kuriqi, Alban] Univ Lisbon, Inst Super Tecn, CERIS, Lisbon, Portugal; [Khazaei, Majid] AREEO, Dept Forests Rangelands & Watershed Management En, Kohgiluyeh & Boyerahmad Agr & Nat Resources Res &, Kohgiluyeh and Boyerahmad, Iran; [Ghorbanzadeh, Omid] Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria
推荐引用方式 GB/T 7714	Avand, Mohammadtaghi,Kuriqi, Alban,Khazaei, Majid,et al. DEM resolution effects on machine learning performance for flood probability mapping[J],2022,40.
APA	Avand, Mohammadtaghi,Kuriqi, Alban,Khazaei, Majid,&Ghorbanzadeh, Omid.(2022).DEM resolution effects on machine learning performance for flood probability mapping.JOURNAL OF HYDRO-ENVIRONMENT RESEARCH,40.
MLA	Avand, Mohammadtaghi,et al."DEM resolution effects on machine learning performance for flood probability mapping".JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 40(2022).