Thunder and lightning approach forecasting method based on particle swarm support vector machine

Abstract

The invention discloses a thunder and lightning approach forecasting method based on a particle swarm support vector machine, relates to the technical field of thunder and lightning forecasting and aims at applying a particle swarm support vector machine method in the thunder and lightning approach forecasting. The method comprises the following steps: carrying out relevance analysis and selecting related factors which influence the occurrence of thunder and lightning from the overhead and ground historical information of an MICAPS (Meteorological Information Comprehensive Analysis and Processing System) and the actual thunder and lightning data of a ground station; preprocessing the data and reasonably interpolating missing data aiming at the characteristics that data which prove whether thunder and lightning occur or not in the thunder and lightning data are imbalanced; optimizing the parameters of the support vector machine by a particle swarm optimizing algorithm; establishing a training sample set, training the support vector machine and establishing a thunder and lightning approach forecasting model; inputting a test data set into the trained forecasting model, so as to judge whether thunder and lightning occur or not. The method has the advantages of high precision and strong generalization capability.

Description

technical field

[0001] The invention relates to the technical field of lightning prediction, in particular to a lightning approach prediction method based on a particle swarm support vector machine. Background technique

[0002] Thunder forecasting involves a lot of relevant information, and so far there is not a very complete model to describe it. In our country weather forecasting still uses more dynamic statistical forecasting methods based on statistics, most of which are based on linear correlation (such as multiple stepwise regression methods and Kalman filter methods used more etc.), so it has obvious deficiencies in dealing with complex nonlinear weather phenomena. In recent years, with the development and improvement of neural networks, people have tried to use neural networks to simulate and predict thunder and lightning weather forecasts, and have achieved certain success. However, there are problems of how to determine the network structure, over-learning and u...

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