A method and apparatus for predicting dangerous weather events based on a multiple incremental regression tree model

An incremental regression and tree model technology, applied in the field of meteorological science, can solve the problems of low prediction accuracy, relying on manual analysis, a lot of computing resources and time, saving computing resources and computing time overhead, and clear and clear physical meaning. , model simple effect

Active Publication Date: 2019-03-15
COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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Problems solved by technology

[0007] The above numerical forecasting methods need to use a lot of computing resources and time, and rely on the manual analysis of forecasters
However, dynamical-statistical methods, support vector machines, and neural netwo

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  • A method and apparatus for predicting dangerous weather events based on a multiple incremental regression tree model

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Embodiment Construction

[0041] The present invention will be further described below through specific embodiments and in conjunction with the accompanying drawings.

[0042]The flow of a dangerous weather event prediction method based on the multiple incremental regression tree model of this embodiment is as follows figure 1 shown. The following takes the forecast of thunderstorm weather events in the next 3 hours across the country as an example to describe in detail.

[0043] The first step is to read the historical data of observation records of surface meteorological stations and establish a sample data set. The sample data set in this embodiment is the national ground meteorological observation data and domestic aviation dangerous weather data from January 2010 to December 2014. The sample data set is a matrix of 30 columns, including stations with thunderstorm weather event records across the country, where each row is an observation record of a station, which is arranged in chronological ord...

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Abstract

The invention relates to a method and a device for predicting dangerous weather events based on a multiple incremental regression tree model. The method comprises the following steps: 1) reading the historical meteorological observation data and taking the meteorological characteristic data and the dangerous weather event record as the sample data set; 2) establish a training data matrix and a verification data matrix according to that sample data set; 3) setting model parameters of the multiple incremental regression tree model; 4) input a training data matrix and a verification data matrix,training that multiple incremental regression tree model to obtain a trained multiple incremental regression tree model; 5) inputting the prediction data matrix to the trained multiple incremental regression tree model to obtain the occurrence probability of the future dangerous weather event. The invention can significantly improve the prediction accuracy rate of the dangerous weather event.

Description

technical field [0001] The invention relates to the field of meteorological science and the field of computer technology, in particular to a method and device for predicting dangerous weather events based on a multiple incremental regression tree model. Background technique [0002] Hazardous weather refers to weather processes that may endanger the safety of flight and ground facilities, generally including: poor visibility, low clouds, cloud cover, high winds, hail, cumulonimbus, thunderstorms, tornadoes or hurricanes. These hazardous weather conditions often lead to catastrophic accidents in aircraft flight. Accurate prediction of dangerous weather events can provide decision-making basis for dangerous weather warnings, ensure flight safety, and reduce casualties and economic losses. [0003] Numerical weather forecasting and dynamic-statistical methods based on numerical forecasting are commonly used in the existing forecasting methods of hazardous weather events. [0...

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Application Information

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IPC IPC(8): G06K9/62G06Q10/04
CPCG06Q10/04G06F18/24323G06F18/214
Inventor 李峥林青慧王学志周园春
Owner COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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