EMD and LSTM fused urban PM2.5 concentration prediction method

Abstract

The invention discloses an EMD and LSTM fused urban PM2.5 concentration prediction method, and relates to the field of air quality concentration prediction. The method comprises the following steps offirstly, acquiring the time sequence data per hour, and performing data cleaning on the acquired data; then, using the EMD (empirical mode decomposition) for carrying out stationary processing on thePM2.5 concentration data to obtain a plurality of components; then, determining a sliding time window T, carrying out data sequence segment segmentation processing on each component, and normalizinga unified dimension to obtain a plurality of data sets; dividing the data set into a training set and a test set, respectively constructing an LSTM network model for training, finally predicting eachcomponent by using the trained model, and carrying out reverse normalization processing on each component to obtain a final urban PM2.5 concentration prediction result; on the basis, constructing a long-term and short-term memory neural network LSTM model and training; finally, using the trained model for prediction, carrying out reverse normalization processing on the model, and obtaining the final urban PM2.5 concentration prediction result.

Description

technical field [0001] The present invention relates to a kind of air quality concentration prediction method, especially relate to a kind of fusion EMD and LSTM deep learning urban PM 2.5 Concentration Prediction Method Background technique [0002] With the rapid development of urbanization and industrialization in our country, most cities, especially the northern regions, have experienced serious air pollution problems. Air pollution not only seriously affects daily traffic, but also causes many health problems, such as respiratory diseases and cardiovascular diseases. Since the Beijing air pollution data was released by the US embassy in 2011, urban residents' awareness of environmental protection has increased day by day, and more and more people have begun to pay attention to the city's air quality issues. With the increasing development of new information technologies such as big data and deep learning, how to use air pollution big data and deep learning technology ...

AI Technical Summary

Problems solved by technology

However, the neural network prediction model can only mine data signals in the same dimension at different time periods, and cannot handle noise smoothly. Therefore, this paper proposes a deep learning time series prediction method that integrates EMD and LSTM

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