Mobile pollution source emission concentration prediction method based on space-time deep learning

A technology of deep learning and emission concentration, applied in the direction of prediction, instrumentation, biological neural network model, etc., can solve the problem of low accuracy in predicting the concentration of air pollutants, and achieve high accuracy and stability

Active Publication Date: 2019-03-19
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] Aiming at the problem that the accuracy of predicting the concentration of air pollutants in the prior art is not high, the presen

Method used

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  • Mobile pollution source emission concentration prediction method based on space-time deep learning
  • Mobile pollution source emission concentration prediction method based on space-time deep learning
  • Mobile pollution source emission concentration prediction method based on space-time deep learning

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Embodiment Construction

[0035] Such as figure 1 and figure 2 Shown, the present invention is concretely realized as follows:

[0036] Considering the spatio-temporal correlation among the 25 monitoring stations and the historical state of the monitoring stations, two or more CNN layers are selected to extract intrinsic features for long-term span learning from historical air pollutant data, followed by one-hot encoding. The method encodes hourly data and combines the extracted features with current weather data and associated pollutant data to improve the model's predictive performance. Two branches are used to extract spatial and temporal features, and then an attention model is used to weigh hidden features to enhance the effectiveness of features. By stacking multiple layers of LSTM, features of spatially correlated pollutant data with long-term dependencies can be automatically extracted layer by layer, and the fused features can be used to predict multi-scale time series forecasting of air po...

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Abstract

The invention discloses a mobile pollution source emission concentration prediction method based on deep learning, and provides a convolutional long-short-term memory neural network prediction methodbased on an attention mechanism according to regional space-time distribution characteristics of mobile pollution source pollutants. Firstly, a Granger causal relationship between stations is analyzedand a hyper-parameter Gaussian vector weight function is developed to determine a spatial autocorrelation variable as a part of an input feature; secondly, extracting time-space characteristics of data used by the LSTM network by using a convolutional neural network, and meanwhile, attention models are respectively used for weighting a characteristic graph and a channel so as to enhance the effectiveness of the characteristics; finally, a time series predictor based on deep LSTM is used to learn long-term and short-term dependency of the atmospheric pollutant concentration. According to the method, inherent useful characteristics are extracted from historical atmospheric pollutant data, and auxiliary data are incorporated into a proposed model to improve the performance, so that the concentration prediction method is realized.

Description

technical field [0001] The invention relates to a data-driven prediction method, in particular to a method for predicting emission concentration of mobile pollution sources based on spatio-temporal deep learning. Background technique [0002] Air pollution, especially the large-scale smog caused by mobile pollution sources ultrafine particles and volatile organic compounds (Volatile Organic Compounds, VOCs) has become one of the most prominent environmental problems in my country. Ultrafine particles and VOCs not only have serious direct harm to human health, but also act as PM 2.5 It plays an important role in the formation of compound air pollution. Therefore, monitoring the emission of ultrafine particles and VOCs from mobile pollution sources is one of the effective means to reduce haze weather and photochemical smog pollution and improve the quality of regional urban air environment. Understanding the sources and quantities of these pollutants is necessary to minimize...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04
CPCG06Q10/04G06Q50/26G06N3/045
Inventor 蒋鹏李永安林宏泽佘青山许欢林广
Owner HANGZHOU DIANZI UNIV
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