Effective Prediction for Rock Burst Dataset Using Classification Algorithms with Particle Swarm

Abstract

Rock burst and Slope Stability is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst and Slope Stability classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst and Slope Stability classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst and Slope Stability indicator for classification. In addition, analysis and prediction of slope stability is of great importance in geotechnical engineering. With the development of economics, the number of slopes is increasing and landslides caused by slope instability have become one of the three major geological disasters in the world along with earthquakes and volcanoes. To reduce or prevent landslide damage, slope stability analysis and stabilization are required. However, accurately predicting slope stability is challenging because of the complexity of slope structures and the difficulty to determine the precise input data associated with key geotechnical parameters the proposed methodology PSO feature extraction preserves important distance relationships, such as : The Random forest, Naive Bayes of each object of the original dataset. This leads to preservation of any mining operation that depends on the ordering of distances between objects, such as Random forest, Naive Bays -search, SVM, J.48 and MLP classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, i.e., tight bounds on the contraction/expansion of the original distances.