Atmospheric pollutant concentration prediction method integrating machine learning with LSTM

Abstract

The invention provides an atmospheric pollutant concentration prediction method integrating machine learning with LSTM. The method comprises the following steps: obtaining atmospheric pollution monitoring data, selecting training data and test data, and completing the data preprocessing; for the prediction target and the training data, constructing an atmospheric pollutant concentration predictionmodel fusing machine learning and LSTM; inputting the training data into a prediction model, and training the prediction model; inputting the test data into the trained model to obtain a prediction result of the test data; analyzing the accuracy of a prediction result of the test data, if the accuracy meets the requirements, performing model fusion and prediction, and if the accuracy does not meet the requirements, adjusting model parameters, and then performing model training. The method has the advantages of simple data, fast calculation speed, full consideration of atmospheric pollutant data, extraction of the time and space distribution characteristics of the data, and high prediction precision.

Description

technical field [0001] The invention belongs to the technical field of air pollutant concentration prediction, and in particular relates to an air pollutant concentration prediction method integrating machine learning and LSTM. Background technique [0002] In recent years, air pollution has gradually become a very serious problem. The continuous deterioration of air quality has caused great harm to people's health and living environment, and people have begun to pay attention to it in their daily lives. Therefore, the prediction of the concentration of air pollutants is very important, but the analysis and prediction of air pollutants are complex, dynamic and random. To make accurate predictions, a large amount of data from multiple fields and departments is involved. Such as meteorological data, industrial data, environmental data, etc. At present, there are a large amount of monitoring data on air pollution sources, pollutants and meteorology in the society. Making full ...

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Problems solved by technology

[0005] At present, the prediction of air pollutant concentration based on neural network mostly adopts BP neural network or LSTM neural network and the combination with other methods, which can find linear or nonlinear relationship and time series relationship in the data, but the spatiotemporal feature mining of data distribution less

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