Next Article in Journal
Wind Condition Analysis of Japanese Rural Landscapes in the 19th Century: A Case Study of Kichijoji Village in Musashino Upland
Previous Article in Journal
Classification of Areas Suitable for Fish Farming Using Geotechnology and Multi-Criteria Analysis
Previous Article in Special Issue
The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes
Open AccessArticle

Predicting Slope Stability Failure through Machine Learning Paradigms

1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway
3
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
4
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5
Department of Civil Engineering, Division of Geotechnical Engineering, Firat University, 23119 Elâzığ, Turkey
6
Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
7
Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 395; https://doi.org/10.3390/ijgi8090395
Received: 5 August 2019 / Revised: 10 August 2019 / Accepted: 2 September 2019 / Published: 4 September 2019
In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems. View Full-Text
Keywords: machine learning; slope failure; finite element analysis; weka machine learning; slope failure; finite element analysis; weka
Show Figures

Figure 1

MDPI and ACS Style

Tien Bui, D.; Moayedi, H.; Gör, M.; Jaafari, A.; Foong, L.K. Predicting Slope Stability Failure through Machine Learning Paradigms. ISPRS Int. J. Geo-Inf. 2019, 8, 395.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop