Stability prediction of Himalayan residual soil slope using artificial neural network
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Stability prediction of Himalayan residual soil slope using artificial neural network Arunava Ray1 · Vikash Kumar1 · Amit Kumar1 · Rajesh Rai1 · Manoj Khandelwal2 · T. N. Singh3 Received: 10 February 2020 / Accepted: 25 June 2020 © Springer Nature B.V. 2020
Abstract 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. Keywords Machine learning · Slope stability · Artificial neural network · Residual soil
* Manoj Khandelwal [email protected]; [email protected] 1
Department of Mining Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India
2
School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Ballarat, Australia
3
Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, India
13
Vol.:(0123456789)
Natural Hazards
1 Introduction The occurrence of landslides depends on the geospatial and geoenvironmental characteristics of an area (Chakraborty and Goswami 2017; Pham et al. 2018; Sazid 2019; Zare et al. 2013). The Himalayan Region (HR) falls in the category of most seismically active mountain chains throughout the globe (Singh et al. 2013). Due to the prevalence of the warmtemperate and subtropical climatic condition, HR has witnessed profound and variable weathering of the bedrock (Vyshnavi et al. 2015). Residual soil is formed after complete rock weathering and disintegration (Regmi et al. 2013). Blight (1977) defines residual soil a
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