Debris flow occurrence probability and scale forecasting method

Abstract

The invention provides a debris flow occurrence probability and scale forecasting method, and solves the data dimension disasters caused by the influence of multiple factors on current debris flow disasters and insufficient forecasting real-time performance caused by the fact that a large number of hidden layer weights need to be calculated in deep learning. The method comprises the following steps: for the prediction of debris flow disaster occurrence probability and debris flow disaster occurrence scale, obtaining initial influence factor data of corresponding debris flow disaster through field investigation and survey, arranging sample data, and extracting corresponding main influence factor data based on an FMPCE algorithm; constructing debris flow disaster occurrence probability and scale forecasting models based on the optimal path forest and based on matrix random approximate singular value decomposition optimization width learning; and inputting the test sample data into the established debris flow disaster occurrence probability prediction model and the debris flow disaster occurrence scale prediction model, and outputting prediction information of the debris flow occurrence probability and scale.

Description

technical field [0001] The invention belongs to the technical field of geological disaster forecasting methods, and in particular relates to a mud-rock flow occurrence probability and scale forecasting method. Background technique [0002] Debris flow is not only a natural disaster, but also a serious engineering geological disaster. In recent years, major geological disasters have occurred frequently, often causing damage to houses, interruption of communication facilities, collapse of roads, destruction of land, and even the destruction of villages. Moreover, accidents often occur in valleys with complex geological structures, vertical and horizontal ravines, and steep terrain, which brings great inconvenience to disaster prevention and post-disaster reconstruction work. The scale and risk are far beyond our tolerance. Therefore, how to use technical means to forecast debris flow disasters has become the core of our concern. [0003] According to the characteristics of d...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for predicting the occurrence probability and scale of debris flow, which aims to solve the data dimension disaster caused by multiple factors in the current debris flow disaster, and the prediction caused by deep learning due to the need to calculate a large number of hidden layer weights The problem of insufficient real-time performance

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