Urban air quality time sequence prediction method considering space-time correlation

A technology related to air quality and space-time, applied in forecasting, structured data retrieval, instruments, etc., can solve problems such as complex and high calculation costs, complex model structure and parameter adjustment process, to improve accuracy and stability, and strengthen forecasting Ability to improve prediction accuracy

Pending Publication Date: 2020-06-26
武汉墨锦创意科技有限公司
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Problems solved by technology

However, although deep learning has excellent data mining performance, its model structure and parameter adjustment process are too complex, requiring a large amount of observation data for training, which also leads to complex and high computing costs.

Method used

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  • Urban air quality time sequence prediction method considering space-time correlation
  • Urban air quality time sequence prediction method considering space-time correlation
  • Urban air quality time sequence prediction method considering space-time correlation

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Embodiment approach

[0034] The present invention proposes such as figure 1 Shown is the method framework, which introduces spatiotemporal correlation cubes to extract spatiotemporal information, and designs singular spectrum analysis and random forest coupled models to accurately fit air quality in future stages. Its specific implementation method is:

[0035] Step 1: Construct an air quality characteristic data set, taking a city as an example, collecting PM2 from January 1, 2017 to January 1, 2018 recorded by 35 urban air quality monitoring stations built in the city before 2018 .5 Monitoring data and meteorological feature data (including temperature, humidity, wind speed, pressure, wind direction, etc.), match the data according to the location of the space station and the collection time, and obtain time series data of different features with the same coordinates and different time stamps ;

[0036] Further, said step 1 includes the following steps:

[0037] Step 1.1: Collect PM2.5 monito...

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Abstract

The invention discloses an urban air quality time sequence prediction method considering space-time relevance. According to the method, singular spectrum analysis is introduced to carry out time sequence prediction on PM2.5 monitoring data and meteorological characteristic data; a space-time correlation cube is designed to adaptively select first K important spatial neighborhood site features, a time sequence prediction result and the first K important spatial neighborhood site features are superposed to construct a sample feature set, and finally fitting of final results under different timescales is completed by using a random forest algorithm. Through the coupling model provided by the invention, the space-time relevance between different space stations can be effectively considered, so that the time sequence prediction effect and the stability degree of a single station in an urban space environment under different time scales are improved, and a reference basis can be provided for urban atmosphere management decision making.

Description

technical field [0001] The present invention relates to the field of atmospheric environment management and monitoring, and more specifically, relates to a time-series prediction method of urban air quality taking into account the temporal-spatial correlation. Time-series prediction method for urban air quality. Background technique [0002] Air pollution is an important environmental health problem. The air quality pollution caused by smog, dust, and inhalable fine particles is constantly endangering the healthy living environment of urban residents, especially for the elderly, children, pregnant women and other sensitive groups. Even worse. In addition, air pollution will also bring many more serious environmental problems, such as acid rain, climate change, water pollution, ecosystem deterioration and so on. Therefore, in order to better meet the needs of assisting government functional departments in decision-making and guiding public life services, it is urgent to pro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06F16/2458G06F16/28G06F16/215
CPCG06Q10/04G06F16/2477G06F16/283G06F16/215
Inventor 关庆锋吕建军姚尧
Owner 武汉墨锦创意科技有限公司
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