Slope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan

Abstract

Slope failures across mountain roads can damage man-made structures, interrupt traffic, and give rise to fatal accidents. Disastrous consequences of these hazards necessitate the approach for predicting their occurrences. In practice, slope collapse prediction can be formulated as a classification problem with two class labels: collapse and noncollapse. This study aims at proposing a novel approach for slope collapse assessment. The newly established method integrates the Bayesian framework and the K-nearest neighbor density estimation technique. The Bayesian framework is employed to achieve probabilistic slope stability estimations. Meanwhile, the K-nearest neighbor technique is a nonparametric approach to approximate the conditional probability density functions. In addition, a database that contains 211 slope evaluation samples has been collected in the Taiwan Provincial Highway Nos. 18 and 21 is used to construct and verify the slope assessment model. Experimental results point out that the proposed model has achieved a roughly 8% improvement in accuracy rate compared with other benchmark methods. Hence, the new method is a promising tool to help decision-makers in slope collapse assessment and disaster prevention planning.