Air quality space-time prediction method based on long-term and short-term memory neural network

Abstract

The invention discloses an air quality space-time prediction method based on a long-term and short-term memory neural network. Particulate matter concentration data of an experiment station and a nearest adjacent station, meteorological data and gaseous pollutant data in the same period are integrated and converted into a supervised learning data format, normalization processing is carried out onthe data, and a prediction sequence of the air mass concentration is obtained by training the data through the long-term and short-term memory network. The method comprises the following steps: S1, acquiring historical air quality data and meteorological data; S2, performing data preprocessing, including abnormal value elimination, missing value interpolation processing, extraction of particulatematter concentration data of adjacent stations and data normalization, on the historical air quality; S3, converting a data format from a sequence to input and output sequence pairs; S4, dividing thedata set into a training set and a test set, and initializing various hyper-parameters of the long-term and short-term memory network; and S5, testing the model effect through prediction on the test set. According to the method, the prediction precision of the air quality data can be improved.

Description

technical field [0001] The invention relates to a long-short-term memory neural network-based spatio-temporal prediction method of air quality, which belongs to the field of air pollution prediction. Background technique [0002] In recent years, with the rapid development of society, the pressure on the environment has been increasing, and some serious air pollution problems have seriously threatened people's health. Therefore, accurate monitoring of air quality can no longer meet people's needs. People hope to predict the air quality situation in advance and make timely warnings and defenses to minimize the threat to life. However, it is very difficult to predict the concentration of air quality, and it is easily affected by other factors, such as meteorological factors (temperature, relative humidity, wind speed and precipitation, etc.), traffic pollution, industrial emissions, etc.; The interplay of instabilities also poses challenges for air quality prediction. In add...

AI Technical Summary

Problems solved by technology

The above models all capture the spatial-temporal correlation of air pollution concentration data very well. However, most of them only consider the impact of meteorological factors on air pollution concentration, but ignore the interaction between air pollutants; because Auxiliary data from all monitoring stations was added, but the interference of unrelated stations negatively affected the accuracy of the model

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