Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

Slope stability prediction research is a complex non-linear system problem. In carrying out slope stability prediction work, it often encounters low accuracy of prediction models and blind data preprocessing. Based on 77 field cases, 5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability. These indicators include slope angle, slope height, internal friction angle, cohesion and unit weight of rock and soil. Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods, namely principal components analysis (PCA), Kernel PCA, factor analysis (FA), independent component analysis (ICA), non-negative matrix factorization (NMF) and t-SNE (stochastic neighbor embedding). Combined with classic machine learning methods, 7 prediction models for slope stability are established and their reliabilities are examined by random cross validation. Besides, the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method. The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability. Random forest (RF), support vector machine (SVM) and k-nearest neighbour (KNN) achieve the best prediction accuracy, which is higher than 90%. The decision tree (DT) has better accuracy which is 86%. The most important factor influencing slope stability is slope height, while unit weight of rock and soil is the least significant. RF and SVM models have the best accuracy and superiority in slope stability prediction. The results provide a new approach toward slope stability prediction in geotechnical engineering. (c) 2022 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Based on the analysis of 77 field cases, this study selects five quantitative indicators (slope angle, slope height, internal friction angle, cohesion, and unit weight of rock and soil) to improve the accuracy of slope stability prediction models. The data aggregation and visualization of slope stability prediction are conducted using six-dimensional reduction methods, and seven prediction models are established and evaluated using random cross validation. The results show that random forest, support vector machine, and k-nearest neighbor algorithms achieve the highest prediction accuracy (>90%), with slope height being the most significant factor influencing slope stability. Random forest and support vector machine models exhibit the best accuracy and superiority in slope stability prediction, providing a new approach for geotechnical engineering.