干旱区生态环境知识资源中心(Arid): Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling

Arid

DOI	10.1016/j.catena.2019.104223
	Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling
	Arabarneri, Alireza 1; Pradhan, Biswajeet 2,3; Lombardo, Luigi 4
通讯作者	Arabarneri, Alireza ; Pradhan, Biswajeet
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2019
卷号	183
英文摘要	The initiation and development of gullies as worldwide features in landscape have resulted in land degradation, soil erosion, desertification, flooding and groundwater level decrease, which in turn, cause severe destruction to infrastructure. Gully erosion susceptibility mapping is the first and most important step in managing these effects and achieving sustainable development. This paper attempts to generate a reliable map using four state-of-the-art models to investigate the Bayazeh Watershed in Iran. These models consists of boosted regression trees (BRT), binary logistic regression (BLR), numerical risk factor (NRF) and frequency ratio (FR), which are based on a geographic information system (GIS). The gully erosion inventory map accounts for 362 gully locations, which were randomly divided into two groups (70% for training and 30% for validation). Sixteen topographical, geological, hydrological and environmental gully-related conditioning factors were selected for modelling. The threshold-independent area under receiver operating characteristic (AUROC) and seed cell area index (SCAI) approaches were used for validation. According to the results of BLR and BRT, the conditioning parameters namely, NDVI and lithology, played a key role in gully occurrence. Validation results showed that the BRT model with AUROC = 0.834 (83.4%) had higher prediction accuracy than other models, followed by FR 0.823 (82.3%), NRF 0.746 (74.6%) and BLR 0.659 (65.9%). SCAI results indicated that the BRT, FR and BLR models had acceptable classification accuracy. The findings, in terms of model and predictor choice, can be used by decision-makers for hazard management and implementation of protective measures in gully erosion-prone areas.
英文关键词	Gully erosion susceptibility GIS Boosted regression trees Binary logistic regression Bayazeh watershed
类型	Article
语种	英语
国家	Iran ; Australia ; South Korea ; Netherlands
收录类别	SCI-E
WOS记录号	WOS:000488417700041
WOS关键词	MULTICRITERIA DECISION-MAKING ; LANDSLIDE SUSCEPTIBILITY ; ENVIRONMENTAL-CHANGE ; HAZARD ASSESSMENT ; CERTAINTY FACTOR ; SEMIARID REGION ; ENSEMBLE ; CATCHMENT ; BIVARIATE ; IRAN
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
EI主题词	2019-12-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/311363
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 3658117994, Iran; 2.Univ Technol Sydney, Fac Engn & Informat Technol, CAMGIS, Sydney, NSW 2007, Australia; 3.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; 4.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands
推荐引用方式 GB/T 7714	Arabarneri, Alireza,Pradhan, Biswajeet,Lombardo, Luigi. Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling[J],2019,183.
APA	Arabarneri, Alireza,Pradhan, Biswajeet,&Lombardo, Luigi.(2019).Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling.CATENA,183.
MLA	Arabarneri, Alireza,et al."Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling".CATENA 183(2019).