Multi-parameter atmospheric environment data generation method based on stacked lstm-gru

Abstract

The invention discloses a multi-parameter atmospheric environment data generation method based on stacked LSTM-GRU. Since the atmospheric environment data samples have the characteristics of many features and huge amount of data, it takes a lot of time to train the LSTM model alone. Compared with LSTM, GRU has a simpler structure, reduced network parameters and faster convergence. The combination of LSTM and GRU can speed up network training on the premise that LSTM can effectively process a large amount of atmospheric environment data. At the same time, because the single-layer network structure is too simple, it is far from meeting the needs of dealing with multiple characteristics of atmospheric environmental data. The multi-parameter atmospheric environment data generation is realized by using a multi-layer network structure.

Description

technical field [0001] The invention relates to the technical field of meteorological monitoring, and more particularly to a method for generating multi-parameter atmospheric environment data based on stacked LSTM-GRU. Background technique [0002] At present, accurate atmospheric environmental data plays an important role in today's world, accompanied by the rapid development of computer technology. The operation and use of weapons and equipment must be completed in the natural environment, and the comprehensive natural environment in which they are located has a complex and profound impact on the combat performance of weapons and equipment. As an important part of the comprehensive natural environment, the atmospheric environment has the characteristics of multi-weather phenomenon, multi-element and time-varying compared with the environment such as space and terrain. The wind speed, wind direction, precipitation, temperature, pressure, humidity, etc. in the atmospheric e...

AI Technical Summary

Problems solved by technology

However, at present, there are few methods of applying deep learning technology to atmospheric environment modeling at home and abroad. Most of the methods are based on a single parameter in the atmospheric environment, and do not consider simultaneously generating multiple atmospheric environment parameters at the same time; and because the atmospheric environment data samples have Due to the characteristics of many features and huge amount of data, the existing model training method needs a lot of time to complete the model training

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