Slope stability prediction method based on improved PSO-RBF algorithm

Abstract

The invention discloses a slope stability prediction method based on an improved PSORBF algorithm. The invention discloses a slope stability prediction model based on an improved PSORBF algorithm, andbelongs to the technical field of slope stability prediction. According to the method, the radial basis function neural network is initialized, the particle swarm optimization algorithm based on thenormal attenuation inertia weight factor is adopted to optimize the radial basis function neural network parameters, and the new radial basis function neural network prediction model is constructed according to the optimal parameters, calculated by the optimization algorithm, of the radial basis function neural network; and the slope stability is predicted by using the improved radial basis function neural network prediction model. According to the method, a radial basis function extension speed control factor is added on the basis of a Gaussian function used by a traditional hidden layer, thefactor can adjust the change trend of parameters of the neural network in the iteration process, sudden change in the iteration process is avoided, and the prediction precision of the trained model is higher.

Description

technical field [0001] The invention belongs to the technical field of slope stability prediction, in particular to a slope stability prediction method based on an improved PSO-RBF algorithm. Background technique [0002] In recent years, with the continuous emergence of extreme weather events around the world, natural disasters have occurred frequently, among which landslides, mudslides and other disasters caused by slope instability have caused the loss of lives and property of many people. Therefore, it is very important to be able to effectively predict the stability of slopes. [0003] The prediction of slope stability is mainly to determine the combination of factors favorable to landslide action through the analysis of landslide conditions, and predict the possibility of landslides in the future on the region or in a certain section of slope according to the combination of these favorable factors. There are four types of slope stability evaluation methods commonly us...

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Problems solved by technology

[0004] Although compared with the original theory, there are still some problems in the PSO algorithm, such as easy to fall into local optimum, slow convergence speed in the later period, and low accuracy of the optimization results of the algorithm; the RBF neural network also has improper parameter settings that will lead to problems in the training model. problems such as underfitting or overfitting

At the same time, because there are many factors affecting the slope stability, if the relevant parameters and quantities cannot be reasonably selected, it is impossible to accurately predict

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