Air quality prediction method for multi-task learning based on multi-dimensional secondary feature extraction

Abstract

The invention provides an air quality prediction method for multi-task learning based on multi-dimensional secondary feature extraction. The air quality prediction method comprises the steps of data acquisition, data preprocessing, pollutant selection, establishment of a convolutional neural network model and a long-term and short-term memory network model for multi-dimensional secondary feature extraction, establishment of a multi-task learning model for multi-dimensional secondary feature extraction and verification. According to the air quality prediction method, the problem that only timeinternal correlation and space internal correlation are considered during traditional spatio-temporal data modeling, and spatio-temporal correlation is not considered is solved. According to the air quality prediction method, influence information related to pollutant values is considered from three perspectives of space, time and space-time, and the prediction deviation is reduced by learning themutual influence among multiple time and space tasks through multi-task learning, so that the prediction precision of time and space models is more accurate.

Description

technical field [0001] The invention belongs to the technical field of air quality prediction, in particular to an air quality prediction method based on multi-task learning of multi-dimensional secondary feature extraction. Background technique [0002] In recent years, due to the increase in energy consumption, the problem of air pollution has become increasingly serious. Air quality prediction is an important modeling task, which has an important impact on many aspects such as agriculture, water resources, transportation, etc. The national environmental protection department has been committed to solving air quality problems. Although it is found that air pollution exceeds the standard, various means of control have achieved certain results, but the current atmospheric environment situation is still very severe, and the prediction of pollutant concentration is of great significance and value for the early warning of serious pollution events. [0003] However, since the a...

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Problems solved by technology

[0008] The purpose of the present invention is to solve the problem that the generally considered spatial correlation dimension is low when predicting air quality, and the correlation between time and space is insufficiently considered, and a method based on multi-dimensional secondary feature extraction is proposed. Air quality prediction method based on multi-task learning

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