Journal: Natural Hazards

Authors: Rajan KC, Milan Aryal, Keshab Sharma, Netra Prakash Bhandary, Richa Pokhrel, Indra Prasad Acharya

Abstract:

Accurate slope stability prediction is of utmost importance to reduce disastrous effects of slope failures and landslides. However, conventional methods of slope stability analysis are complex and challenging, and more importantly, use of these methods in a wide-area slope stability assessment requires a large number of soil property and field investigation data. These complexities and challenges often demand some simplified statistical slope stability analysis models such as by using machine learning (ML) techniques. So, in this research, we develop slope stability prediction models using multiple linear regression (MLR) and artificial neural network (ANN) and classify the slopes as safe or unsafe using random forest (RF) and support vector machine (SVM) methods. For this purpose, a dataset of 4,208 slope cases was created using limit equilibrium-based Slide software. The effectiveness of each model was then …