A debris flow prediction method based on PCA and a mixed kernel function LSSVR

Abstract

The invention discloses a debris flow prediction method based on PCA and a mixed kernel function LSSVR, and the method comprises the steps of firstly, building a debris flow monitoring and early warning system, obtaining an influence factor of an initial debris flow disaster, and carrying out the dimensionality reduction of the obtained initial influence factor through PCA; secondly, constructinga mixed kernel function LSSVR debris flow disaster model by utilizing the initial influence factors after dimension reduction; then, applying the whale algorithm to optimize the established mixed kernel function LSSVR debris flow disaster model, and obtaining the optimized combined model parameters; and finally, reconstructing a mixed kernel function LSSVR mud-rock flow disaster model by using theobtained combined model parameters, and outputting a mud-rock flow occurrence prediction result. According to the method disclosed by the invention, the complexity of a model structure is greatly reduced, and a dimensionality disaster is prevented; a hybrid kernel function mechanism is introduced to balance model learning ability and generalization ability, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of geological disaster monitoring, in particular to a method for predicting debris flow based on PCA and mixed kernel function LSSVR. Background technique [0002] Debris flow is one of the common geological disasters in mountainous areas. Because of its wide distribution, high frequency of occurrence, and rapid disaster speed, it seriously threatens the safety of life and property of people in mountainous areas and the sustainable development of the economy and society. Therefore, it has become the focus of people's attention to provide an effective debris flow disaster forecasting method. [0003] Researchers in related fields have conducted in-depth research on the characteristics of debris flow disasters, and proposed a variety of debris flow disaster prediction methods, each of which has advantages and disadvantages. Cao Lulai et al. used the combination of fuzzy system theory and artificial neural net...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a kind of debris flow prediction method based on PCA and mixed kernel function LSSVR, which solves the disaster of prediction dimension caused by the influence of multiple factors in the occurrence of current debris flow disasters, and the problem of selecting a single factor in the least squares support vector regression model. Partial defects in model training performance and low accuracy caused by kernel functions

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