Classification of slope stability based on Artificial Neural Network and Naive Bayes

Abstract

This paper presents the prediction of slope stability using machine learning (ML) methods which are Artificial Neural Network (ANN) and Naive Bayes (NB) classifier. The prediction models were developed based on six input factors namely “unit weight, internal friction angle, cohesion, slope angle, slope height and pore pressure ratio” and factor of safety (FOS) as the output factor. The slope data was collected from the previous studies and divided into 70% training and 30% testing datasets for both models. The classification process of ANN and NB were implemented using python programming and the result shows that ANN prediction model gives better prediction result with accuracy of 95%, compared to NB with 84% of accuracy.