A Classification Method for Thunderstorm Gale Scale Prediction Based on Multi-source Convolutional Neural Network
A technology of neural network and classification method, which is applied in biological neural network models, neural architectures, instruments, etc., can solve problems such as difficult to improve the robustness of prediction and classification, model overfitting, lack of softmax classifier and difficult to classify sample processing, etc. Achieve good prediction effect, improve extraction and classification effect
Active Publication Date: 2021-02-12
绍兴达道生涯教育信息咨询有限公司 +1
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The cross-entropy loss function will make the model overfit. In many cases, the intra-class distance of the extracted feature vector is greater than the inter-class distance.
The existing methods also lack the processing of samples that are not easy to classify by the softmax classifier, and for small sample data sets, the prediction of the softmax classifier is prone to overfitting, and it is difficult to improve the robustness of the prediction classification
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[0085] In this embodiment, the flow process of the thunderstorm gale grade prediction and classification method based on the multi-source convolutional neural network is as follows figure 1 As shown, the basic steps are as above S1-S3. The implementation process of each step is described in detail below.
[0086] (1) if figure 2 As shown, the process of generating training samples based on Doppler weather radar image data is:
[0087] (1-1) Query the historical data of the automatic weather station. In the automatic weather station database of a certain province, select a certain area (121.5094 degrees east longitude, 30.0697 degrees north latitude) as the center, within 220 kilometers (maximum measurement range 230 kilometers) including station information of all types of automatic weather stations inside and outside the province. In chronological order, the hourly maximum wind speed is counted from the data of these automatic weather stations, and the corresponding time ...
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The invention discloses a thunderstorm and gale grade prediction and classification method based on a multi-source convolutional neural network. This method uses a multi-source convolutional neural network model to extract features from a variety of data images obtained by Doppler meteorological mines, which can integrate more meteorological data information and improve the extraction of differential features; at the same time, the method combines the support vector The classification method in the machine, the model obtained on the training set of meteorological data with small and medium samples has a good prediction and classification effect of thunderstorm and gale level.
Description
technical field [0001] The invention relates to the fields of image processing and disaster weather prediction, in particular to a local thunderstorm and gale level prediction method based on a multi-source convolutional neural network and a support vector machine. Background technique [0002] my country is one of the countries with frequent occurrence of strong convective weather, heavy precipitation, hail, thunderstorms, strong winds and tornadoes. With the development of the economy, the losses caused by the occurrence of strong convective weather will be more serious. Severe convective weather is one of the difficulties and key points in the current weather forecasting business due to its small time-space scale, rapid change, severe weather, large social impact, and complicated occurrence and development mechanism. [0003] The monitoring of severe convective weather is an important part of severe convective weather forecasting, especially the basis of short-term nowca...
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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/21G06F18/214G06F18/2411G06F18/24G06F18/254
Inventor 姚金良姜艳萍钱峥王荣波谌志群黄孝喜
Owner 绍兴达道生涯教育信息咨询有限公司