Space-time related air quality prediction method

A technology of air quality and forecasting methods, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems that the neural network does not take into account the local spatial characteristics and insufficient consideration of factors affecting air quality, so as to improve the forecasting Accuracy, make up for the effect of small quantity

Pending Publication Date: 2019-12-20
HARBIN ENG UNIV
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Problems solved by technology

[0004] In view of the insufficient consideration of the factors affecting air quality, the feature extraction adopts empirical extraction and manual definition, and the traditional neural network does not

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  • Space-time related air quality prediction method
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[0033] Specific embodiment one:

[0034] according to figure 1 As shown, the present invention provides a space-time correlation air quality prediction method, comprising the following steps:

[0035] Step 1: Divide the city into grids of the same size, and divide them into prediction areas and estimation areas according to whether there are air detection stations in the grids;

[0036] The specific method of grid division is as follows:

[0037] The city is divided into grid areas, and the overall area of ​​the city is divided into non-intersecting square grids R with side length c i , where each sub-grid belongs to a part of the city's total grid, that is, R i ∈R, each grid area and its eight adjacent network areas constitute the influence area, the latitude and longitude of the center of each grid is used as the coordinates of the grid, and the grid is the basic unit for estimation and prediction.

[0038] Step 2: Obtain time-series data related to data affecting air qu...

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Abstract

The invention relates to a space-time related air quality prediction method. The method comprises: dividing a city into grids with the same size, each grid being influenced by grids in adjacent areas,and dividing the grids into a prediction area and an estimation area according to whether air monitoring stations exist in the grids or not; obtaining related time series data influencing the air quality data, conducting feature extraction on the time series data through a recurrent neural network and spatial deep learning, and conducting time series model training; obtaining related non-time-series data influencing the air quality data, conducting feature extraction on the non-time-series data through a convolutional neural network, and conducting space model training; performing cooperative training on the time sequence model training and the space training model to obtain a prediction model; training a prediction area by using the trained collaborative training model to obtain air quality data of the prediction area; and training an estimation grid region by using the trained collaborative training model to obtain air quality data of the estimation region.

Description

technical field [0001] The invention relates to the technical field of air quality prediction, and relates to a time-space related air quality prediction method. Background technique [0002] In recent years, with the rapid increase of population and the rapid development of economy, automobile exhaust and pollutants emitted by factories and enterprises are discharged into the air. The air pollution caused by this has become a hot issue of social concern. Therefore, accurate air quality prediction data Can provide a reliable basis for air pollution control. In order to grasp the air pollution situation, the government has established air monitoring stations to monitor the air quality of the region in real time, but the number of air monitoring stations is limited, and it is impossible to carry out full-scale coverage monitoring. At the same time, the monitoring stations cannot predict the future air quality conditions and cause damage Air pollution traceability analysis. T...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06F16/951
CPCG06Q10/04G06N3/08G06F16/951G06N3/045
Inventor 韩启龙荆海航宋洪涛张海涛张慧苗禹杨在强
Owner HARBIN ENG UNIV
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