干旱区生态环境知识资源中心(Arid): Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Arid

DOI	10.3390/rs12172833
	Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
	Arabameri, Alireza; Nalivan, Omid Asadi; Pal, Subodh Chandra; Chakrabortty, Rabin; Saha, Asish; Lee, Saro; Pradhan, Biswajeet; Dieu Tien Bui
通讯作者	Lee, S
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:17
英文摘要	The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.
英文关键词	land degradation gully erosion random partitioning approaches machine learning algorithm jackknife test
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000569601000001
WOS关键词	EVIDENTIAL BELIEF FUNCTION ; SUPPORT VECTOR MACHINE ; LOGISTIC-REGRESSION ; SOIL-EROSION ; NEURAL-NETWORKS ; SEDIMENT PRODUCTION ; SEMIARID REGION ; DECISION TREE ; PREDICTION ; BIVARIATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326313
作者单位	[Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran; [Nalivan, Omid Asadi] Gorgan Univ Agr Sci & Nat Resources GUASNR, Dept Watershed Management, Gorgan 3184761174, Golestan, Iran; [Pal, Subodh Chandra; Chakrabortty, Rabin; Saha, Asish] Univ Burdwan, Dept Geog, Bardhaman 713104, W Bengal, India; [Lee, Saro] Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; [Lee, Saro] Korea Univ Sci & Technol, Dept Geophys Explorat, 217 Gajeong Ro, Daejeon, South Korea; [Pradhan, Biswajeet] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Ultimo, NSW 2007, Australia; [Pradhan, Biswajeet] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; [Pradhan, Biswajeet] King Abdulaziz Univ, Ctr Excellence Climate Change Res, POB 80234, Jeddah 21589, Saudi Arabia; [Pradhan, Biswajeet] Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Chan...
推荐引用方式 GB/T 7714	Arabameri, Alireza,Nalivan, Omid Asadi,Pal, Subodh Chandra,et al. Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility[J],2020,12(17).
APA	Arabameri, Alireza.,Nalivan, Omid Asadi.,Pal, Subodh Chandra.,Chakrabortty, Rabin.,Saha, Asish.,...&Dieu Tien Bui.(2020).Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility.REMOTE SENSING,12(17).
MLA	Arabameri, Alireza,et al."Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility".REMOTE SENSING 12.17(2020).