CNN and LSTM fused neural network-based air PM2.5 concentration prediction method

A PM2.5, concentration prediction technology, applied in biological neural network model, prediction, neural architecture, etc., can solve the problem of inability to extract data deep connections, lack of data analysis, not well suited to changing air pollution conditions, etc. problems, to achieve the effect of achieving environmental management level and high accuracy

Inactive Publication Date: 2018-05-08
SHANGHAI NORMAL UNIVERSITY
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Problems solved by technology

These traditional forecasting methods have had outstanding performance in this type of forecasting work, but lack of deeper analysis of the data, so that the deep connection of the data cannot be extracted; on the other hand, the concentration of polluta

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  • CNN and LSTM fused neural network-based air PM2.5 concentration prediction method
  • CNN and LSTM fused neural network-based air PM2.5 concentration prediction method

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Embodiment Construction

[0040] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0041] The present invention first defines the air pollutant concentration prediction:

[0042] Definition 1 Prediction of air pollutant concentration: mainly through historical pollutants and meteorological information, to predict the concentration of a series of air pollution such as PM2.5 and PM10 in a certain period of time in the future. It is one of the key research topics, so it has certain interdisciplinary nature.

[0043] Definition 2 Traditional forecasting methods: non-deep learning air pollutant concentration forecasting methods are collectively referred to as traditiona...

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Abstract

The invention relates to a CNN and LSTM fused neural network-based air PM2.5 concentration prediction method. The method comprises the steps of S1: building a city PM2.5 concentration prediction modelbased on a deep learning principle, a CNN and LSTM; S2: for the built model, selecting training data and test data from environment monitoring data, and finishing initialization of the prediction model; S3: training the model by utilizing the training data; S4: by utilizing the trained model, obtaining a test prediction result according to test data; S5: judging the accuracy of the test prediction result, and if the accuracy exceeds a threshold, executing the step S6, or otherwise, returning to the step S2; and S6: performing prediction by utilizing the trained model. Compared with the priorart, the prediction accuracy of the prediction method is higher than that of a conventional prediction method; and under the same working duration and working conditions, a better result can be generated.

Description

technical field [0001] The invention relates to a method for predicting air quality concentration, in particular to a method for predicting air PM2.5 concentration based on CNN and LSTM fusion neural network. Background technique [0002] Air pollution is widely concerned in daily life. In particular, pollutants such as PM2.5, which have small particle size, large area, strong activity, are prone to be accompanied by harmful substances such as heavy metals and microorganisms, are difficult to eliminate in the atmosphere, and travel long distances, need to be paid more attention to. At present, the problem of air pollution is becoming more and more prominent. The analysis and prediction of air pollution are complex and dynamic, involving multiple departments, regions and fields. Accurate prediction of air pollution requires the processing of a large amount of related environmental data and environmental information. Therefore, under the current situation, in the face of a wi...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G01N15/06
CPCG06Q10/04G06Q50/26G01N15/06G06N3/045
Inventor 张波雍睿涵李美子赵勤秦东明
Owner SHANGHAI NORMAL UNIVERSITY
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