Deep learning models for large-scale slope instability examination in Western Uttarakhand, India

Slope failures are avoidable accidents in most of the scenarios. The eventuality of a failure leads to loss of lives and destruction, especially in hilly areas. Investigation, analysis and prediction of slope failure is a reliable approach to avert such mishaps. Hence, the present research work delves into the prediction of landslides and slope failures through numerical simulation and a deep learning approach. Field attributes and laboratory-tested strength data from Lower Tons Valley, Northern India has been taken as a case study. Initially, a total of 185 slope models were simulated in a finite difference code by varying four slope parameters, namely, slope angle, slope height, cohesion and angle of internal friction. These simulated results were further divided into two parts, one part with 148 datasets for the training of models and other part consisting of 37 datasets for testing of models. Two artificial neural network prediction models, along with a conventional multi-linear regression model was developed and their accuracy was accessed. The developed neural network models superseded the conventional model, in terms of performance and accuracy, as shown by statistical approaches R and mean squared error values. Moreover, the neural network model with Adam optimizer achieved higher statistical accuracy than the one with stochastic gradient descent optimizer. However, all these deep learning models demonstrate significant performance, and can be used by geo-engineers for swift prediction of safety factors for excavated slopes in the study area.