A PM2.5 Prediction Method Based on Deep Structure Recurrent Neural Network

Abstract

The invention discloses a PM2.5 prediction method based on a deep structure recurrent neural network. A large amount of collected data is used to construct a PM2.5 prediction model with a deep structure according to deep learning and recurrent neural network theory. Through data feature extraction and Training to realize the prediction of haze weather, aiming to improve the efficiency and accuracy of haze prediction, and provide persuasive decision-making basis for haze prevention and governance. The prediction model has almost no requirements for the data structure, as long as the data is large enough, it can learn by itself, making deep learning very suitable for the needs of the current Internet big data applications.

Description

technical field [0001] The invention belongs to the technical field of environmental engineering and detection, and more particularly, relates to a PM2.5 prediction method based on a deep structure cyclic neural network. Background technique [0002] Air quality has always been a major issue related to the future and destiny of mankind. With social progress and the sharp increase in car ownership, the content of inhalable particulate matter in the air has risen sharply, and the problem of environmental pollution has become increasingly serious. With the continuous deterioration of air quality, more and more smog weather phenomenon, more and more harmful. Smog is a catastrophic weather phenomenon. Inhalable particulate matter PM2.5 is the main cause of haze weather, which not only has a serious impact on air quality, but also poses a huge threat to human health. [0003] There are many ideas and methods for air quality prediction research. Among the many methods, based on t...

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Problems solved by technology

[0004] Due to the influence of a large number of uncertain and complex factors such as climate, temperature, and human activities, the time series of various weather data is highly nonlinear and uncertain, and it is difficult for conventional analysis and prediction methods to grasp the changes. Regularity and Variation Characteristics

[0005] The shallow neural network is effective in solving simple or more restricted problems, but due to limited modeling and representation capabilities, it has limited ability to realize some more complex problems involving natural signals in real life.

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