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

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...

AI Technical Summary

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.

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