globalchange  > 气候变化与战略
DOI: 10.1007/s11069-020-04213-3
论文题名:
Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India
作者: Chakrabortty R.; Pal S.C.; Sahana M.; Mondal A.; Dou J.; Pham B.T.; Yunus A.P.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 104, 期:2
起始页码: 1259
结束页码: 1294
语种: 英语
中文关键词: 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
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168422
Appears in Collections:气候变化与战略

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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

Recommended Citation:
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]. Natural Hazards,2020-01-01,104(2)
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