气候变化领域集成服务门户(CCPortal): Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

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DOI	10.1007/s11069-020-04213-3
	Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India
	Chakrabortty R.; Pal S.C.; Sahana M.; Mondal A.; Dou J.; Pham B.T.; Yunus A.P.
发表日期	2020
ISSN	0921030X
起始页码	1259
结束页码	1294
卷号	104 期号:2
英文摘要	Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy over short time periods. So, keeping this scenario in view, the main objective of this work is to identify areas vulnerable to soil erosion and propose the most suitable model for soil erosion susceptibility in subtropical environment. The implementation of machine learning and artificial intelligence techniques with a GIS environment for determining erosion susceptibility is highly acceptable in terms of optimal accuracy. The point-specific values of different elements from random sampling were considered for this study. Sensitivity analysis of the predicted models (i.e., analytical neural network, geographically weighted regression and GWR–ANN ensemble) was performed using the maximum causative factors and related primary field observations. The area under curve of receiver operating system reveals precision with 87.13, 89.57 and 91.64 for GWR, ANN and ensemble GWR–ANN, respectively. The ensemble GWR–ANN is more optimal than the GWR, ANN for determining water-induced soil erosion susceptibility. The process of soil erosion is not a unidirectional process, so the multidimensional impacts from the conditioning factors have to be determined precisely by considering the maximum possible factors as well as selecting optimal models for specific regions. © 2020, Springer Nature B.V.
关键词	Ensemble GWR–ANN Land degradation Random sampling Soil erosion Unidirectional process
英文关键词	artificial intelligence; artificial neural network; ensemble forecasting; GIS; land degradation; machine learning; natural hazard; sampling; soil erosion; India
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205623
作者单位	Department of Geography, The University of Burdwan, Barddhaman, West Bengal 713104, India; School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom; Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Barddhaman, West Bengal 713104, India; Key Laboratory of Geological Hazards On Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang, 443002, China; Department of Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata, 940-2188, Japan; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; State Key Laboratory of Geo-Hazard Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, 610059, China
推荐引用方式 GB/T 7714	Chakrabortty R.,Pal S.C.,Sahana M.,et al. Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India[J],2020,104(2).
APA	Chakrabortty R..,Pal S.C..,Sahana M..,Mondal A..,Dou J..,...&Yunus A.P..(2020).Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India.Natural Hazards,104(2).
MLA	Chakrabortty R.,et al."Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India".Natural Hazards 104.2(2020).