The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes.

Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.