DOI: 10.1007/s11069-020-04141-2
论文题名: Stability prediction of Himalayan residual soil slope using artificial neural network
作者: Ray A. ; Kumar V. ; Kumar A. ; Rai R. ; Khandelwal M. ; Singh T.N.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 103, 期: 3 起始页码: 3523
结束页码: 3540
语种: 英语
中文关键词: Artificial neural network
; Machine learning
; Residual soil
; Slope stability
英文关键词: artificial neural network
; machine learning
; prediction
; residual soil
; slope dynamics
; slope stability
; stability analysis
; Himalayas
; Siwalik Hills
英文摘要: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168694
Appears in Collections: 气候变化与战略
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作者单位: Department of Mining Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India; School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Ballarat, Australia; Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, India
Recommended Citation:
Ray A.,Kumar V.,Kumar A.,et al. Stability prediction of Himalayan residual soil slope using artificial neural network[J]. Natural Hazards,2020-01-01,103(3)