CN109492830A - A kind of mobile pollution source concentration of emission prediction technique based on space-time deep learning - Google Patents

A kind of mobile pollution source concentration of emission prediction technique based on space-time deep learning Download PDF

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CN109492830A
CN109492830A CN201811541300.5A CN201811541300A CN109492830A CN 109492830 A CN109492830 A CN 109492830A CN 201811541300 A CN201811541300 A CN 201811541300A CN 109492830 A CN109492830 A CN 109492830A
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蒋鹏
李永安
林宏泽
佘青山
许欢
林广�
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Abstract

The mobile pollution source concentration of emission prediction technique based on deep learning that the invention discloses a kind of, the present invention propose the convolution shot and long term Memory Neural Networks prediction technique based on attention mechanism according to the region temporal-spatial distribution characteristic of mobile pollution source pollutant.Firstly, by the Granger causality between analysis website and developing hyper parameter Gaussian vectors weighting function to determine spatial autocorrelation variable as a part of input feature vector.Secondly, extracting the when space characteristics of the data of LSTM Web vector graphic using convolutional neural networks, while attention model is respectively used to weighted feature figure and channel with the validity of Enhanced feature.Finally, the time series forecasting device based on depth LSTM, for learning the long-term and short-term dependence of pollutant.The present invention extracts intrinsic useful feature from history atmosphere pollution data, and auxiliary data is included in proposed model to improve performance, thus concentration prediction method.

Description

A kind of mobile pollution source concentration of emission prediction technique based on space-time deep learning
Technical field
The present invention relates to a kind of prediction technique of data-driven more particularly to a kind of mobile dirts based on space-time deep learning Contaminate source emission concentration prediction method.
Background technique
Atmosphere pollution, especially mobile pollution source superfine particulate matter and volatile organic matter (Volatile Organic Compounds, VOCs) caused by wide range of haze have become one of China's environmental problem most outstanding.Superfine particulate matter and VOCs not only has serious direct harm to human health, while as PM2.5Important as precursors object and photochemical fog master Component part is wanted, the formation of compound atmosphere pollution is played a crucial role.Therefore, mobile pollution source ultra-fine grain is monitored The discharge amount of object and VOCs is to reduce haze weather and photochemical pollution, improves having for region city atmospheric environment quality One of effect means.It is necessary to understand the source of these pollutants and quantity, to reduce atmosphere pollution to the greatest extent to the unfavorable shadow of health It rings.On Spatial Dimension, the feature of the pollutant variation of different regions is analyzed, dissects pollutant concentration in time and sky Between on otherness and Crack cause, and then improve pollutant concentration prediction efficiency and accurate reliability, be government control Atmosphere pollution and traffic control, living trip provide decision-making foundation.
In general, air pollution processes are usually directed to a plurality of types of pollutants of interaction, and by local reaction, The Spatio-temporal Evolution characteristic of air pollutant concentration and the influence of Confounding Factor, such as the direction of wind and humidity.Therefore, to air dirt The research of dye object concentration prediction still faces following two challenge: (1) LSTM network is generally used for temporal characteristics extraction, but its disadvantage It is the having differences property when list entries has long-rang dependence and complexity task.Model is difficult to learn reasonable vector It indicates, keeps the learning efficiency of model poor；(2) air pollution causal path is complicated between the different location of nature. Correlation between monitoring station is very complicated, because it may be by geography, natural process, the shadow of meteor or other factors It rings.
Summary of the invention
The present invention provides a kind of base for the not high problem of the accuracy of air pollutant concentration is predicted in the prior art In the mobile pollution source concentration of emission prediction technique of space-time deep learning.
A kind of mobile pollution source concentration of emission prediction technique based on space-time deep learning, comprising the following steps:
Step 1: solving the problems, such as temporal correlation using granger-causality test, Granger causality is as measure time The index to interact between sequence, in recent decades always by favor.For complicated space factor, using Granger because Fruit relationship analyzes the time series of air concentration.The time series of the air pollutants of one monitoring station is defined as Yi, separately The time series of one monitoring point is Xi.The format and null hypothesis of Granger causality are:
This is NdThe neighborhood collection of space clustering (considers that all space factors carry out monitoring point using K-Means algorithm Cluster)；εtIt is a white Gaussian random vector, n is the quantity of timestamp, vector ΦiIt is corresponding weight Yi；μiRepresentation space Position XiAnd YiBetween space weight.
However, atmosphere pollution is also different to the dependence of varying environment condition, pollutant is in diffusion process vulnerable to wind To influence.Therefore, it is necessary to study the space-time causalities of different pollutants under wind directions different between different monitoring points.Based on Lower example such as Fig. 1, it can be seen that the causality analysis between room and time should reflect two aspects: the 1) dependence of space-time structure Property, it reflects propagation of the various pollutants on room and time；2) predictability shows that different environmental conditions may be led Cause different space-time causalities.
For XiAnd YiSpace weight mui, wind direction and distance are combined with the concentration diffusion process of air pollutants, mentioned Go out a kind of hyper parameter Gauss weight vectors based on gaussian kernel function, can be described as:
Wherein α (j) is learning rate, wherein dX/dYAnd θXYIt respectively represents apart from variable and angle variables.Distance can pass through Euclidean distance directly calculates.First formula is determined using F inspection, and whether than second formula is more important.If so, XiIt is YiCausality, so XiIt can be used for predicting YiNote that having used a bandwidth parameter σ, it indicates transformed Compromise between direction and distant effect；
Step 2: space time feature extraction, usage history air pollutant concentration data establish a time-space relationship and are Input of the three-dimensional matrice as CNN.Assuming that the monitoring station S Si={ s1,s2,…,s25Sum be spatially arranged sequentially section On.The station S can provide one group of time detection data Di={ dt-r,…,dt-2,dt-1, wherein the inspection of t record at every point of time Measured data isD is time detection data, and S is monitoring station, and ο is attribute.According to the sky of monitoring point on road Between and Annual distribution be combined with all data, obtained a size be M={ S, D, ο } three-dimensional data matrix.This paper's is pre- Survey problem can be with is defined as: for one group of given monitoring station S in given different time intervals, by its newest maximum time Step t-r ..., and t-2, t-1 } historical data is modeled for temporal correlation and air pollutant concentration prediction.In order to improve effect Rate, addition batch standardizes after second and third convolutional layer of model.Used here as nonlinear activation function SELU function, SELU has better convergence and the problem of gradient disappears can be effectively avoided, this is specific in Klambauer et al. It discusses.
Step 3: establishing characteristic pattern attention model, carry out convolution algorithm using convolution kernel to obtain output room and time Feature Mapping.In attention model, F={ f (1), f (2) ..., f (j) } is that Feature Mapping, i.e. j are hidden in the output of convolutional layer The quantity of ∈ R convolution kernel.It is calculated using following equation by equation:
F '=W ⊙ F
Here, W={ ω1,ω2,…,ωjIt is one group of weight matrix, size is identical as Feature Mapping.Generate the note of W Meaning power model is made of three convolutional layers with stride 1.First convolutional layer has k × s filter, convolution kernel having a size of 5 × 5, second and third layer have k filter, convolution kernel is having a size of 3 × 3, and each convolutional layer has 100 filters.⊙ is The product of element.
Step 4: establishing channel attention model, the three-dimensional space-time data matrix of input model includes many different categories Property, different attributes has different influences to the estimated performance of model.It is expected that this 34 kinds of useful informations are integrated into machine learning In model, the study and analysis that have supervision are carried out, realizes accurately prediction.Meanwhile the different channels output one from convolutional layer It is fixed that there is different influence on model.Therefore, different channels is weighted, to improve feature to model prediction performance Validity.
F={ f (1), f (2) ..., f (j) } is mapped for input feature vector, wherein j is the port number in Feature Mapping, in F The maximum pondization operation of middle execution is to obtain each channel C={ c1,c2,…,cjMaximum value.Attention mechanism will generate attention Weight vectors V and weighting indicate that the following equation of F ' calculates
ci=maxf (j)
V=softmax (C)
F '=V ⊙ F
Step 5: multiple monitoring point air pollution concentration predictions obtain weight temporal feature and sky from convolution sum merging treatment Between after feature, F can be passed throughST=F 's⊕F′tIt by element adds two characteristic patterns and compresses them into one-dimensional characteristic X =(x1,x2,…,xt).These features are broken down into continuous component and are fed to duplicate LSTM unit to carry out the time point Analysis.Then using LSTM layers from X=(x1,x2,…,xt) extracting time information input, and another input is walked from final time Suddenly the hidden unit h startedt-1.The forward direction training process of Attention-CNN-LSTM can be indicated with following equation:
ft=σ (Wf·g[ht-1,xt]+bf)
it=σ (Wi·g[ht-1,xt]+bi)
Ct=ft×Ct-1+it×tanh(WC·g[ht-1,xt]+bC)
ot=σ (Wo·g[ht-1,xt]+bo)
ht=ot·tanh(Ct)
Wherein it, otAnd ftRespectively indicate input gate, out gate and the activation for forgeing door；CtAnd htRespectively indicate each cell With the activation carrier of block of memory；W and b respectively indicate weight matrix and bias vector.Then, only by the defeated of the final step of LSTM It is fed in the layer being fully connected out, is used for space-time Air Pollution Forecast.
Training terminates, and the weighting parameter of model determines, model at this time is using granger-causality test spatial coherence Wind direction influence factor is added in analysis, weighs hiding feature using attention model with the validity of Enhanced feature, it will be real-time Weather information, contaminant information and timestamp information be input in model as mobile pollution source emissions concentration, model The concentration results of i.e. exportable following 24 hours multi-scale predictions.
In prediction technique of the invention, the Granger causality between analysis website is used, and generate spatial data As opposite website and time-space attribute, space characteristics and temporal characteristics are extracted using common convolution unit, and by using attention Power mechanism is weighted feature, by stacked multilayer LSTM, can successively automatically extract the space phase with long-rang dependence The feature of pollutant data is closed, and it is pre- come the multiple dimensioned time series for predicting air pollutant concentration that fusion feature can be used It surveys.The method achieve higher Stability and veracities.
Detailed description of the invention
Fig. 1 is pollutant spatial Correlation Analysis figure；
Fig. 2 is based on space-time deep learning mobile pollution source concentration prediction model structure chart.
Specific embodiment
As depicted in figs. 1 and 2, the method is specifically implemented by the following steps:
In view of the historic state of temporal correlation and monitoring station between 25 monitoring stations, select two or more CNN layers are extracted the internal characteristics of long-term span study from history air pollutants data, then using single heat coding.The party Method encodes data per hour, and the feature of extraction is mutually tied to current meteorological data and relevant pollutant data It closes, to improve the estimated performance of model.Space characteristics and temporal characteristics are extracted using Liang Ge branch, then use attention model To weigh hiding feature with the validity of Enhanced feature.By stacked multilayer LSTM, can successively automatically extract have for a long time according to Rely property space correlation pollutant data feature, and can be used fusion feature come predict air pollutants it is multiple dimensioned when Between sequence prediction.
Step 1: solving the problems, such as temporal correlation using granger-causality test, Granger causality is as measure time The index to interact between sequence, in recent decades always by favor.For complicated space factor, using Granger because Fruit relationship analyzes the time series of air concentration.The time series of the air pollutants of one monitoring station is defined as Yi, separately The time series of one monitoring point is Xi.The format and null hypothesis of Granger causality are:
This is NdThe neighborhood collection of space clustering (considers that all space factors carry out monitoring point using K-Means algorithm Cluster)；εtIt is a white Gaussian random vector, n is the quantity of timestamp, vector ΦiIt is corresponding weight Yi；μiRepresentation space Position XiAnd YiBetween space weight.
However, atmosphere pollution is also different to the dependence of varying environment condition, pollutant is in diffusion process vulnerable to wind To influence.Therefore, it is necessary to study the space-time causalities of different pollutants under wind directions different between different monitoring points.Based on Lower example such as Fig. 1, it can be seen that the causality analysis between room and time should reflect two aspects: the 1) dependence of space-time structure Property, it reflects propagation of the various pollutants on room and time；2) predictability shows that different environmental conditions may be led Cause different space-time causalities.
For XiAnd YiSpace weight mui, wind direction and distance are combined with the concentration diffusion process of air pollutants, mentioned Go out a kind of hyper parameter Gauss weight vectors based on gaussian kernel function, can be described as:
Wherein α (j) is learning rate, wherein dX/dYAnd θXYIt respectively represents apart from variable and angle variables.Distance can pass through Euclidean distance directly calculates.First formula is determined using F inspection, and whether than second formula is more important.If so, XiIt is YiCausality, so XiIt can be used for predicting Yi；Note that having used a bandwidth parameter σ, it indicates transformed Compromise between direction and distant effect.
Step 2: space time feature extraction, usage history air pollutant concentration data establish a time-space relationship and are Input of the three-dimensional matrice as CNN.Assuming that the monitoring station S Si={ s1,s2,…,s25Sum be spatially arranged sequentially section On.The station S can provide one group of time detection data Di={ dt-r,…,dt-2,dt-1, wherein the inspection of t record at every point of time Measured data isD is time detection data, and S is monitoring station, and ο is attribute.According to the sky of monitoring point on road Between and Annual distribution be combined with all data, obtained a size be M={ S, D, ο } three-dimensional data matrix.This paper's is pre- Survey problem can be with is defined as: for one group of given monitoring station S in given different time intervals, by its newest maximum time Step t-r ..., and t-2, t-1 } historical data is modeled for temporal correlation and air pollutant concentration prediction.In order to improve effect Rate, addition batch standardizes after second and third convolutional layer of model.Used here as nonlinear activation function SELU function, SELU has better convergence and the problem of gradient disappears can be effectively avoided.
Step 3: establishing characteristic pattern attention model, carry out convolution algorithm using convolution kernel to obtain output room and time Feature Mapping.In attention model, F={ f (1), f (2) ..., f (j) } is that Feature Mapping, i.e. j are hidden in the output of convolutional layer The quantity of ∈ R convolution kernel.It is calculated using following equation by equation:
F '=W ⊙ F
Here, W={ ω1,ω2,…,ωjIt is one group of weight matrix, size is identical as Feature Mapping.Generate the note of W Meaning power model is made of three convolutional layers with stride 1.First convolutional layer has k × s filter, convolution kernel having a size of 5 × 5, second and third layer have k filter, convolution kernel is having a size of 3 × 3, and each convolutional layer has 100 filters.⊙ is The product of element.
Step 4: establishing channel attention model, the three-dimensional space-time data matrix of input model includes many different categories Property, different attributes has different influences to the estimated performance of model.It is expected that this 34 kinds of useful informations are integrated into machine learning In model, the study and analysis that have supervision are carried out, realizes accurately prediction.Meanwhile the different channels output one from convolutional layer It is fixed that there is different influence on model.Therefore, different channels is weighted, to improve feature to model prediction performance Validity.
F={ f (1), f (2) ..., f (j) } is mapped for input feature vector, wherein j is the port number in Feature Mapping, in F The maximum pondization operation of middle execution is to obtain each channel C={ c1,c2,…,cjMaximum value.Attention mechanism will generate attention Weight vectors V and weighting indicate that the following equation of F ' calculates
ci=maxf (j)
V=softmax (C)
F '=V ⊙ F
Step 5: multiple monitoring point air pollution concentration predictions obtain weight temporal feature and sky from convolution sum merging treatment Between after feature, F can be passed throughST=F 's⊕F′tIt by element adds two characteristic patterns and compresses them into one-dimensional characteristic X =(x1,x2,…,xt).These features are broken down into continuous component and are fed to duplicate LSTM unit to carry out the time point Analysis.Then using LSTM layers from X=(x1,x2,…,xt) extracting time information input, and another input is walked from final time Suddenly the hidden unit h startedt-1.The forward direction training process of Attention-CNN-LSTM can be indicated with following equation:
ft=σ (Wf·g[ht-1,xt]+bf)
it=σ (Wi·g[ht-1,xt]+bi)
Ct=ft×Ct-1+it×tanh(WC·g[ht-1,xt]+bC)
ot=σ (Wo·g[ht-1,xt]+bo)
ht=ot·tanh(Ct)
Wherein it, otAnd ftRespectively indicate input gate, out gate and the activation for forgeing door；CtAnd htRespectively indicate each cell With the activation carrier of block of memory；W and b respectively indicate weight matrix and bias vector.Then, only by the defeated of the final step of LSTM It is fed in the layer being fully connected out, is used for space-time Air Pollution Forecast.
Training terminates, and the weighting parameter of model determines, model at this time is using granger-causality test spatial coherence Wind direction influence factor is added in analysis, weighs hiding feature using attention model with the validity of Enhanced feature, it will be real-time Weather information, contaminant information and timestamp information be input in model as mobile pollution source emissions concentration, model The concentration results of i.e. exportable following 24 hours multi-scale predictions.
Step 7: the space-time deep learning model obtained to training is analyzed and is compared, and is analyzed and is compared, compared to Other existing methods, can be preferably to the mobile pollution source emission of solution with the prediction model based on space-time deep learning Concentration temporal correlation, and there is preferable prediction accuracy.
The above examples are only used to illustrate the technical scheme of the present invention and are not limiting, reference only to preferred embodiment to this hair It is bright to be described in detail.Those skilled in the art should understand that can modify to technical solution of the present invention Or equivalent replacement should all cover in claim model of the invention without departing from the spirit and scope of the technical solution of the present invention In enclosing.

Claims (1)

1. a kind of mobile pollution source concentration of emission prediction technique based on space-time deep learning, it is characterised in that: this method is specific The following steps are included:
Step 1: solving the problems, such as temporal correlation using granger-causality test, for complicated space factor, use Granger Causality analyzes the time series of air concentration；The time series of the air pollutants of one monitoring station is defined as Yi, The time series of another monitoring point is Xi；The format and null hypothesis of Granger causality are:
Wherein NdThe neighborhood collection of space clustering, εtIt is a white Gaussian random vector, n is the quantity of timestamp, vector ΦiIt is pair The weight Y answeredi, μiRepresentation space position XiAnd YiBetween space weight；
For XiAnd YiSpace weight mui, wind direction and distance are combined with the concentration diffusion process of air pollutants, proposed A kind of hyper parameter Gauss weight vectors based on gaussian kernel function, description are as follows:
Wherein α (j) is learning rate, wherein dX/dYAnd θXYIt respectively represents apart from variable and angle variables, distance passes through euclidean Distance directly calculates, and first formula is determined using F inspection, and whether than second formula is more important；If so, XiIt is YiBecause Fruit relationship, so XiIt can be used for predicting Yi, bandwidth parameter σ, it indicates the compromise between transformed direction and distant effect；
Step 2: space time feature extraction, usage history air pollutant concentration data establish the three-dimensional of a time-space relationship Matrix, the input as CNN；Assuming that the monitoring station S Si={ s1,s2,…,s25Sum be spatially arranged sequentially on section；S One group of time detection data D can be provided by standingi={ dt-r,…,dt-2,dt-1, wherein the testing number of t record at every point of time According to beingD is time detection data, and S is monitoring station, and ο is attribute；According to the space of monitoring point on road and Annual distribution is combined with all data, has obtained the three-dimensional data matrix that a size is M={ S, D, ο }；Thus forecasting problem Is defined as: for one group of given monitoring station S in given different time intervals, by its newest maximum time step { t- R ..., t-2, t-1 } historical data is modeled for temporal correlation and air pollutant concentration prediction；In order to improve efficiency, in mould Batch is added after second and third convolutional layer of type to standardize；
Step 3: establishing characteristic pattern attention model, carry out convolution algorithm using convolution kernel to obtain output room and time feature Mapping；In attention model, F={ f (1), f (2) ..., f (j) } is that Feature Mapping, i.e. j ∈ R volume are hidden in the output of convolutional layer The quantity of product core；It is calculated using following equation by equation:
F '=W ⊙ F
Here, W={ ω1,ω2,…,ωjIt is one group of weight matrix, size is identical as Feature Mapping；Generate the attention of W Model is made of three convolutional layers with stride 1；First convolutional layer has k × s filter, and convolution kernel is having a size of 5 × 5, and the Two and third layer have k filter, convolution kernel is having a size of 3 × 3, and each convolutional layer has 100 filters；⊙ is element Product；
Step 4: establishing channel attention model
F={ f (1), f (2) ..., f (j) } is mapped for input feature vector, wherein j is the port number in Feature Mapping, is held in F Row maximum pondization operation is to obtain each channel C={ c1,c2,…,cjMaximum value；Attention mechanism, which will generate, pays attention to weight Vector V and weighting indicate that the following equation of F ' calculates
ci=maxf (j)
V=softmax (C)
F '=V ⊙ F
Step 5: multiple monitoring point air pollution concentration predictions obtain weight temporal feature from convolution sum merging treatment and space are special After sign, pass through FST=F 's⊕F′tIt by element adds two characteristic patterns and compresses them into one-dimensional characteristic X=(x1, x2,…,xt)；These features are broken down into continuous component and are fed to duplicate LSTM unit to carry out time analysis；Then Using LSTM layers from X=(x1,x2,…,xt) extracting time information input, and another input is since final time step Hidden unit ht-1；The forward direction training process of Attention-CNN-LSTM can be indicated with following equation:
ft=σ (Wf·g[ht-1,xt]+bf)
it=σ (Wi·g[ht-1,xt]+bi)
Ct=ft×Ct-1+it×tanh(WC·g[ht-1,xt]+bC)
ot=σ (Wo·g[ht-1,xt]+bo)
ht=ot·tanh(Ct)
Wherein it, otAnd ftRespectively indicate input gate, out gate and the activation for forgeing door, CtAnd htRespectively indicate each cell and note Recall the activation carrier of block, W and b respectively indicate weight matrix and bias vector；Then, only the output of the final step of LSTM is presented It is sent in the layer being fully connected, is used for space-time Air Pollution Forecast；
Training terminates, and the weighting parameter of model determines, model at this time is using granger-causality test spatial Correlation Analysis Middle addition wind direction influence factor weighs hiding feature using attention model with the validity of Enhanced feature, by real-time gas Image information, contaminant information and timestamp information are input in model as mobile pollution source emissions concentration, model Export the concentration results of following 24 hours multi-scale predictions.
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