Abstract
An accurate slope prediction model is important for slope reinforcement before the disaster. The k-nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.
Highlights
Landslide is a complex natural phenomenon of slope instability, and it usually causes huge losses to human life and property
Predicting the slope stability is still a challenge. e factors that influence the slope stability are various and complicated, and the main influence factors can be roughly divided into three categories [2], including physical and mechanical properties of the slope soil, natural topography, and external factors
Zhao et al [5] chose six input variables—density, friction angle, friction coefficient, slope angle, slope height, and pore water pressure—for the prediction of slope stability using the relevance vector machine method and found that the RVM is a robust tool for the prediction of slope stability
Summary
Landslide is a complex natural phenomenon of slope instability, and it usually causes huge losses to human life and property. Lin et al [4] chose six typical slope parameters—unit weight, cohesion, internal friction angle, slope inclination, slope height, and pore water ratio—to establish the evaluation index system and predicted the slope stability using four supervised learning methods. Zhao et al [5] chose six input variables—density, friction angle, friction coefficient, slope angle, slope height, and pore water pressure—for the prediction of slope stability using the relevance vector machine method and found that the RVM is a robust tool for the prediction of slope stability. Samui and Kothari [6] chose six input variables—unit weight, cohesion, angle of internal friction, slope angle, height, and pore water pressure coefficient—for the prediction of slope stability using the least square support vector machine method and found that the developed LSSVM is a robust model for slope stability analysis. We improved the KNN algorithm to reduce its sample dependence and improve the robustness of the algorithm and built the prediction model of the slope
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