PM2.5 concentration space-time change prediction method and system based on space-time diagram neural network

A technology of neural network and time-space change, which is applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems that the time relationship does not consider the spatial relationship, and the accuracy needs to be improved, so as to achieve the effect of improving the prediction accuracy

Active Publication Date: 2022-01-11
北京航天创智科技有限公司
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Therefore, most of the existing methods only consider the temporal relationshi...

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  • PM2.5 concentration space-time change prediction method and system based on space-time diagram neural network
  • PM2.5 concentration space-time change prediction method and system based on space-time diagram neural network
  • PM2.5 concentration space-time change prediction method and system based on space-time diagram neural network

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[0085] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0086] Provide a method for predicting the spatiotemporal change of PM2.5 concentration based on spatiotemporal graph neural network, combined with Figure 1-2, including the following steps:

[0087] (1) Obtain the historical data of atmospheric pollutant concentration monitoring of each atmospheric monitoring station, the meteorological data of national weather stations, forecasted meteorological data and elevation data....

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Abstract

The invention relates to a PM2.5 concentration spatio-temporal change prediction method and system based on a spatio-temporal diagram neural network. The method comprises steps of acquiring atmospheric pollutant concentration monitoring historical data of each atmospheric monitoring station, meteorological data of a national meteorological station, predicted meteorological data and elevation data; constructing a sample, and constructing an adjacent matrix M and a weight matrix W by using the longitude and latitude of each atmosphere monitoring station; and constructing a neural network model based on the space-time diagram to predict a prediction result of each station, and obtaining a corresponding PM2.5 concentration prediction value. According to the method, observation data of about 1500 atmospheric monitoring stations in the whole country is taken as a training set, various data sources such as meteorology and elevation are combined, the neural network based on the space-time diagram is used, a unified prediction framework is constructed, the PM2.5 concentration change in a large area can be predicted at the same time, and the prediction precision is improved.

Description

technical field [0001] The present invention relates to the technical field of meteorological forecasting, in particular to a method and system for predicting temporal and spatial changes in PM2.5 concentration based on temporal and spatial graph neural networks. Background technique [0002] Air pollution is one of the major environmental issues affecting health. Accurate short-term prediction of air pollution can provide a basis for government decision-making, make timely preventive measures, and reduce the occurrence of pollution incidents. [0003] Existing PM2.5 prediction methods are mainly divided into mechanism models and data-driven methods. The mechanism model can predict by simulating known physical laws and inputting the required historical observation data to initialize the model. However, due to the fact that the mechanism process of pollutant formation has not been fully ascertained and is also limited by computing resources, it cannot be fully simulated. , ...

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Application Information

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IPC IPC(8): G06F30/27G06F17/16G06N3/04G06N3/08G06F113/08
CPCG06F30/27G06F17/16G06N3/049G06N3/08G06F2113/08G06N3/044Y02A90/10
Inventor 徐崇斌左欣王鑫磊吴俣陈前孙晓敏杨勇刘亮
Owner 北京航天创智科技有限公司
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