CN109214592A - A kind of Air Quality Forecast method of the deep learning of multi-model fusion - Google Patents
A kind of Air Quality Forecast method of the deep learning of multi-model fusion Download PDFInfo
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Abstract
The present invention discloses a kind of Air Quality Forecast method of the deep learning of multi-model fusion, one, acquisition history air quality data and meteorological data；Two, missing values interpolation and normalized are carried out to history air quality data；Three, using deep learning model of the history air quality data building based on seq2seq as single factor test prediction model；Four, the seq2seq deep learning model based on double attention mechanism is constructed as multi-factor Estimation Model using history air quality data and meteorological data；Five, single factor test prediction model prediction result, multi-factor Estimation Model prediction result and the current weather data of air quality data are dissolved into xgboost boosted tree and carry out recurrence calculating, the predicted value of final air quality data is obtained, the method for the present invention can be improved the precision of prediction of air quality data.
Description
Technical field
The invention belongs to the crossing domains of Computer Subject and Environmental Science, and in particular to a kind of depth of multi-model fusion
The Air Quality Forecast method of study.
Background technique
Since social progress recent years, rapid industrial development result in a large amount of severe environmental pollution problem, especially
That air pollution is aggravating always, the pellet in air is more and more, the air quality that people increasingly live by
Gradually decline.In aerial pellet, category PM2.5 and PM10 is the most serious, not only generates to air quality very big
It influences, can also will cause very big injury to human body.In order to reduce injury of the air pollutants to human body, air quality is divided
Analysis has important practical significance with prediction.
For this problem of Air Quality Forecast, the researcher that there are many recent years is made that a large amount of contribution.He
Combine newest thought and new technology to realize quantitative research, make it possible to the reason of finding out air pollution and air quality
Basic trend.The wherein Effects of Factors there are many air qualities, and there is many uncertainty and complexity.Conventional
Artificial neural network, backpropagation (Back Propagation, BP) neural network and regression prediction method are not enough to accurately
Prediction, it is also difficult to learn its internal rule.
Wang Xin etc. is using LSTM (long short term memory network) Recognition with Recurrent Neural Network to fault time
Sequence is predicted；It wears Li Jie etc. and carries out PM2.5 short-term concentration dynamic forecasting using machine learning；Niu Yuxia etc. is calculated using heredity
Method and backpropagation (Back Propagation, BP) neural network carry out Air Quality Forecast；Model completes Xiang etc. and uses circulation mind
Air pollution space-time forecasting model is established through network (Recurrent Neural Network, RNN)；Zheng Yi etc. establishes depth letter
Read the PM2.5 prediction model of network；It opens day etc. and carries out Air Quality Forecast using BP neural network.
Since monitoring station is when obtaining air quality data, generally because equipment fault or network delay Caton etc. are asked
Topic, causes air quality data more missing values occur.On traditional data prediction, especially missing values are handled, and are filled out
Mending the most of method of missing values is the methods of deletion, averaging method and neighbouring method, and the precision filled up is poor, however air quality
Data band has timing information, and it is poor to result in sequence data precision in training.Prediction model uses traditional list in the choice
One model, it may appear that the problems such as over-fitting or low precision, compared to the engineerings such as artificial neural network and traditional support vector machines
Model is practised, the prediction model based on deep learning can be in terms of the feature of capture data and precision of prediction preferably.For sequence
There are timing information in data, the extraction that traditional machine learning method is difficult timing information therein, and use deep learning
Technology can extract temporal aspect, to improve the precision of prediction.
Summary of the invention
In view of this, the present invention provides a kind of Air Quality Forecast method of the deep learning of multi-model fusion, it can
Improve the precision of prediction of air quality data.
Realize that technical scheme is as follows:
A kind of Air Quality Forecast method of the deep learning of multi-model fusion, comprising the following steps:
Step 1: obtaining history air quality data and meteorological data；
Step 2: carrying out missing values interpolation and normalized to history air quality data；
Step 3: being based on the depth of seq2seq (Sequence to Sequence) using the building of history air quality data
Learning model is spent as single factor test prediction model；
Step 4: using history air quality data and meteorological data building based on double attention (attention) mechanism
Seq2seq deep learning model as multi-factor Estimation Model；
Step 5: history air quality data input single factor test prediction model is obtained the prediction knot of single factor test prediction model
History air quality data and meteorological data input multi-factor Estimation Model are obtained the prediction knot of multi-factor Estimation Model by fruit
The prediction result of single factor test prediction model, the prediction result of multi-factor Estimation Model and current weather data are dissolved by fruit
Recurrence calculating is carried out in xgboost boosted tree, obtains the predicted value of final air quality data.
Further, air quality data includes PM2.5, PM10, NO2、CO、O3And SO2It is more than middle a kind of or two classes dense
Degree evidence.
Further, when air quality data is the concentration data of two classes or more, in step 5, air matter is successively predicted
One kind in data is measured, by the prediction result of the single factor test prediction model of such air quality data and multi-factor Estimation Model
Prediction result and current weather data, which are dissolved into xgboost boosted tree, carries out recurrence calculating, obtains such air quality number
According to predicted value.
Further, meteorological data includes temperature, air pressure, humidity, wind direction, wind speed and weather index.
Further, missing values interpolation is carried out using desired maximization approach.
Further, the input layer of single factor test prediction model is history air quality data, and the structure of hidden layer is yes
Seq2seq model, inside are coding and decoding structures, and coding and decoding unit therein is LSTM long short-term memory nerve net
Network, output layer are Air Quality Forecast value.
Further, the input layer of multi-factor Estimation Model is history air quality data and meteorological data, hidden layer knot
Structure be a kind of double attention mechanism seq2seq model, inside be a kind of coding-decoding (encoder-decoder) structure,
It is separately added into one layer of attention mechanism before coding (encoder) and decoding (decoder), output layer is air quality data
Predicted value.
The utility model has the advantages that
1, the present invention is in the building of single factor test prediction model and multi-factor Estimation Model, for the phase for influencing air quality
It closes the factor and makes analysis, then extract important feature, the input as model.
2, the present invention has used the seq2seq model of double attention mechanism in the selection of model, is to circulation nerve net
The extension of network (RNN), can be good at processing sequence data, can be right by the self-teaching of neural network rule therein
Following trend is made prediction.
3, the present invention is directed to the complexity of Air Quality Forecast, has merged the prediction result of multi-model, and common cooperation is to ask
Optimal solution is solved, to acquire optimal predicted value, completes the Accurate Prediction to air quality.
4, present invention incorporates the special self attributes of air quality, using EM EM algorithm to air quality number
According to Missing Data Filling is carried out, it can be good at the distribution of fitting data.
Detailed description of the invention
Fig. 1 is model structure of the present invention.
Fig. 2 is single factor test prediction model figure of the present invention.
Fig. 3 is multi-factor Estimation Model figure of the present invention.
Specific embodiment
The present invention will now be described in detail with reference to the accompanying drawings and examples.
The present invention needs to model air data, very important when before modeling to the selection of characteristic, especially
It is air quality data, the influence being related on space-time, such as time upper daily commuter time section, the week in each week
End, several days of the front and back of every month, each season (summer and winter air pollution level are different).Influence spatially simultaneously, than
As each monitoring station will receive the influence of the weather conditions of surrounding area, the influence of wind direction, the influence of humidity.Therefore, exist respectively
Model is established on time and Spatial Dimension, establishes unifactor model on time dimension first, is chosen single factor test (such as PM2.5) and is made
For the input of unifactor model, temporal trend is found out, obtains local rule；Then multifactor mould is established on Spatial Dimension
Type chooses the input of multifactor (surrounding area weather conditions etc.) as Multiple-Factor Model, obtains global rule；Finally using melting
Molding type combines the weather conditions of current time using the output of unifactor model and Multiple-Factor Model as input, calculates power
Weight, obtains optimal predicted value.As shown in Figure 1, the specific steps are as follows:
Step 1: obtaining historical weather data, including PM2.5 hourly, PM10, NO2, CO, O3, SO2, temperature, gas
Pressure, humidity, wind direction, wind speed, weather index, wherein temperature unit: DEG C, the barometric millimeter of mercury: hundred pas (hPa), humidity unit: percentage
Number (%), wind direction unit: the north starts clockwise angle definition, wind speed unit: m/s, weather index: fine day rains, cloudy
Deng.
Step 2: being pre-processed to historical data
2.1, since air quality data is all by outdoor sensor come in special circumstances or artificial feelings
Under condition, sensors for data inaccuracy or sensor stop collecting data, and air quality data is caused to have missing values.Cause
This needs to carry out missing values interpolation or completion to historical data when doing Air Quality Forecast.
Missing value complement is carried out using expectation maximization (expectation maximization algorithm, EM) method
Entirely.EM algorithm is the maximum likelihood and maximum a posteriori estimate to the probability parameter model containing hidden variable, wherein being handle
Air quality data is maximized as hidden variable, and according to likelihood formula, obtains the parameter expression of model, is then taken true
Data are iterated solution wherein parameter, finally calculate missing values.
2.2, historical data is normalized
Wherein, X is initial data, XminThat indicate is the minimum value in initial data, XmaxWhat is indicated is in initial data
Maximum value.
2.3, the historical data handled is proportionally divided, is divided into 80% training set and 20% test set.
The present invention carries out interpolation processing to missing values using greatest hope EM algorithm, and EM algorithm has time series data relatively good
Capability of fitting, very close to the distribution of initial data.
Step 3: building single factor test prediction model: model structure is divided into input layer, hidden layer, output layer, wherein input layer
Data be PM2.5, PM10, NO2、CO、O3, SO2Six kinds of concentration values are K × 6 as training data, the dimension of input layer.Output
Layer is PM2.5, PM10, NO2、CO、O3, SO2The predicted value of concentration, the dimension of output layer are 6 × T.
Wherein K indicate choose length of time series, that is, choose K time frame air quality data (PM2.5, PM10,
NO2、CO、O3, SO2), such as K=72h (hour).T indicates the air quality data in future time instance, that is, indicates prediction future T
The air quality data at moment, such as T=48h (hour).
Unifactor model hidden layer structure: this hidden layer uses seq2seq (Sequence to Sequence) model, makes
With coding-decoding (encoder-decoder) structure, wherein coding encoder structure is the long short-term memory an of multilayer
LSTM (Long Short-Term Memory) neural unit can calculate a context after coding (encoder)
(context) vector, for expressing the important feature of input data.Decoding (decoder) is partially mainly used to decode, and structure is
The LSTM unit of multilayer.
Step 4: building multi-factor Estimation Model: model structure is divided into input layer, hidden layer, output layer, wherein input layer
Data be entire historical weather data, including PM2.5, PM10, NO2、CO、O3、SO2, temperature, air pressure, humidity, wind direction, wind
12 kinds of data such as speed, weather index, the dimension of input layer are K × 12, and wherein K indicates the length of time series chosen, such as K=
72h (hour).Output layer is air quality data predicted value, and dimension is 6 × T, T indicates the air quality data of future time instance,
Including PM2.5, PM10, NO2、CO、O3、SO2Six kinds of concentration datas, such as T=48h (hour).
The hidden layer structure of Multiple-Factor Model: this model is a kind of seq2seq (the Sequence to of double attention mechanism
Sequence) model, inside be a kind of coding-decoding (encoder-decoder) structure, before the coding be added one layer note
Power (attention) mechanism of meaning, to obtain the contextual information before coding, the part coding encoder later is more than one
LSTM (Long Short-Term Memory) neural unit of layer can calculate one after coding (encoder)
Context vector, for expressing the important feature of input data.One layer of attention is also added before decoding at this time
(attention) mechanism.Decoding (decoder) is partially mainly used to export last result phase, and structure is the LSTM of multilayer
Unit.
Step 5: fusion forecasting model: use xgboost (eXtreme Gradient Boosting) boosted tree as
Training pattern successively trains PM2.5, PM10, NO2、CO、O3And SO2One of which in six kinds of air quality concentration datas, often
When secondary trained, the input of data is single factor test prediction model and the Multiple-Factor Model prediction of a certain air quality concentration data
Prediction result, while the weather conditions at nearest moment are also inputted, including temperature, air pressure, humidity, wind direction, wind speed, weather index
Corresponding labeled data is current each air quality data (PM2.5, PM10, NO when Deng, training2、CO、O3、SO2), such as
Training when input data be PM2.5 (unifactor model result), PM2.5 (Multiple-Factor Model result), temperature, air pressure, humidity,
Eight kinds of data characteristicses such as wind direction, wind speed, weather index (six kinds of current weather conditions), then labeled data is that current PM2.5 is dense
Degree evidence, and so on, remaining five class (PM10, NO can be obtained2、CO、O3、SO2) input data and labeled data, every
A kind of input data and labeled data, is input to xgboost boosted tree.Xgboost model training when inside establish n
Return subtree, that is, have n tree node, and each tree node has multiple leaf nodes, wherein leaf node be single factor test with it is more
Eight kinds of characteristic values of the inputs such as the predicted value of factor prediction model and nearest moment meteorological data, and each tree node is corresponding with
Which leaf node will the weight according to existing for eight characteristic values of input determined, the principle of node split be so that
Select the maximum feature of information gain as split point, the leaf node of subtree each in this way can have different degree (weight
Value), the training of each round adjusts leaf node weighted value according to objective function, forms new subtree, reaches after successive ignition
Optimal subtree, each children tree nodes are predicted values, and all children tree nodes predicted values are all added up, air can be obtained
Quality predictions.After training input data and labeled data input xgboost, it is pre- that six air qualities can be respectively obtained
Measured value, including PM2.5, PM10, NO2、CO、O3And SO2Six kinds of concentration datas.
Prediction result assessment
In order to which the result finally predicted is evaluated and analyzed, obtained prediction model is carried out using test set data
Detection and evaluation are used as evaluation index, root-mean-square error using root-mean-square error (root mean square error, RMSE)
Formula as shown by the equation:
Wherein,That indicate is predicted value, yiWhat is indicated is original value, and n indicates the sum of original value.
The unicity of evaluation index in order to prevent simultaneously, uses R-square as the evaluation index of regression analysis, wherein
The formula of R-square is as shown by the equation:
Wherein,That indicate is predicted value, yiWhat is indicated is original value,What is indicated is average value, and n indicates original value
Sum.
Embodiment 1
Technical solution in order to preferably explain the present invention chooses Beijing 36 air quality monitoring station in the present invention
The data of data and 18 weather stations, and sufficient sample data is obtained, specific implementation steps of the invention are as follows:
Step 1: obtaining data
The air quality data that the present invention uses is to have collected Beijing's in the January, 2017 of counting per hour in January, 2018
According to wherein air quality data mainly includes following several important air pollutants: PM2.5 (μ g/m3), PM10 (μ g/m3),
NO2(μ g/m3), CO (mg/m3), O3(μ g/m3) and SO2(μg/m3).And there are also weather meteorological datas, mainly include weather,
Temperature, air pressure, humidity, wind speed, wind direction.Wherein weather mainly includes fine day, snowy day, cloudy day, light rain, heavy rain, sand etc.；Temperature
It is the temperature value that weather station monitors, unit is degree Celsius；Air pressure refers to atmospheric pressure, and unit is hundred pas (hPa)；Humidity refers in air
Vapor content, unit is percentage (%)；Wind speed refers to that the speed for the wind that weather station monitors, unit are metre per second (m/s) (m/
s)；Wind direction refers to the source of wind, such as north wind refers to the wind blown from north orientation south, wind direction by since the north clockwise angle determine
Justice.Such as the wind blown from south, wind direction is 180 degree, and the wind blown from east, wind direction is 90 degree.
Step 2: data prediction
For history air quality data, can inevitably there be monitoring instrument and go wrong, final data is caused to lack,
It enters data into before prediction model, first carries out the interpolation processing of missing values, the method for the interpolation that the present invention uses is maximum
It is expected that (EM) method, asks maximal possibility estimation and M to walk in sample and asks maximization to maximum likelihood result comprising E step.Given instruction
Practicing sample is { x1..., xm, independent between sample, target will find the implicit classification z of each sample, so that maximum likelihood function p
(x, z) is maximum.
It is to calculate log-likelihood function expectation for E step, maximal possibility estimation is carried out to hidden variable:
Qi(z(i)) :=p (z(i)|x(i)；θ)
Wherein, QiTo give x(i)With parameter θ about z(i)Posterior probability, solve and how to select Q (z) that likelihood can be made
The function problem equal with its lower bound, is exactly to enable Q in facti(z(i)) it is z(i)Posterior probability.
It is to maximize the expected result of E step likelihood function for M step, it is newest to obtain new desired value
Desired value can be applied to missing values, carries out missing values and fills up.Q is selected in E stepiSeek the infimum of log-likelihood function:
E step and M walk continuous circulating repetition, until convergence.
Data after interpolation processing are normalized using min-max method for normalizing, and normalization can be fine
Ground reduces data in a certain range, and can be with the convergence rate of nondimensionalization, quickening model solution.The present invention is number
According to diminution between [0,1], normalized formula are as follows:
Wherein, X is original weather data, XminThat indicate is the minimum value in original weather data, XmaxWhat is indicated is former
Maximum value in beginning weather data.
The data that pretreatment is completed are divided, training dataset and test data set, ratio 8:2 are divided into.
Step 3: construction single factor test prediction model
Single factor test prediction model input data is PM2.5, PM10, NO2、CO、O3And SO2Six kinds of concentration as training data,
The dimension of input layer is K × 6, and K indicates the length of time series chosen.
Unifactor model uses seq2seq model, and internal structure is coding-decoding (encoder-decoder), gives
Sequence x={ x1,x2,…,xTCoded portion formula specifically:
ht=f (ht-1,xt)
Wherein, t is current time, htFor in the hidden state of t moment, f is LSTM encoder.
After encoder carries out coding completion, all htGroup is combined into vector c, as the input of decoder, lsb decoder
The formula divided specifically:
h(t)=f (h(t-1),y(t-1),c)
p(yt|y(t-1),y(t-2),...y(1), c) and=g (h(t),y(t-1),c)
Wherein, h(t)For the output of encoder, c is the last one state in encoder, and f and g are nonlinear activation function,
It is set as LSTM neural network unit.
Illustraton of model is partially made of 2 layers of Recognition with Recurrent Neural Network (RNN) as shown in Fig. 2, wherein encoding (encoder), each
Layer 64 LSTM neural unit of setting.And decode (decoder) and be partially made of 2 layers of Recognition with Recurrent Neural Network (RNN), each layer
64 LSTM neural units are set.
The training parameter setting of model is as shown in table 1:
1 model training parameter of table
Step 4: construction multi-factor Estimation Model
The input of multi-factor Estimation Model is entire historical weather data, including PM2.5, PM10, NO2、CO、O3、SO2, temperature
12 kinds of data such as degree, air pressure, humidity, wind direction, wind speed, weather index, the dimension of input layer are K × 12, and wherein K indicates selection
Length of time series.
Multiple-Factor Model is to improve it to the improvement of seq2seq model using the seq2seq model of double attention mechanism
The influence to other meteorologic factors to air quality can preferably be learnt afterwards, key message can be grabbed, to preferably learn
The sequential relationship between data is practised, there is stronger robustness.
Its encoder section gives K sequence dataA feedforward neural network is constructed,
Attention mechanism is added, specific formula is as follows:
Wherein ht-1And st-1For last hidden state and neuron
State, w and u are learning parameter.
Ensured using softmax function allThe sum of weight be equal to 1,
Wherein,It is attention weight of the k sequence in t moment, there are these to pay attention to weight, we can be adaptively
It extracts
Next it is encoded using the LSTM neural network of multilayer
Wherein f is LSTM neural network unit,It is the new sequence with attention weight, htFor the hiding shape of t moment
State.
The state h obtained after being encoded for encodert, attention mechanism is added, as follows:
Ensured using softmax function allThe sum of weight be equal to 1, it is as follows:
WhereinIt is the weight of i moment encoder output state.
Attention weight and encoder hidden state { h1,h2,…hTWeighted sum as context vector ct:
Its decoder section uses the LSTM of multilayer as decoder, once obtain weighted sum context vector c, so that it may
With by they and given target sequence (y1,y2,…,yT-1) combine:
WhereinWithIt is related to the size of decoder input, newlyIt is exactly new decoder states, state value is defeated
Enter in decoder, as follows:
Wherein g is LSTM neural network unit, dtFor last output state, as decoder section.
Illustraton of model is as shown in figure 3, be wherein added attention mechanism (attention) before coding (encoder), and encode
Part is made of 2 layers of Recognition with Recurrent Neural Network (RNN), 64 LSTM neural units of each layer of setting.Attention is added before decoding
Mechanism (attention), and decode (decoder) and be partially made of 2 layers of Recognition with Recurrent Neural Network (RNN), each layer is also provided with 64
It is a
LSTM neural unit.
Step 5: consolidated forecast model
Regression forecasting is carried out using xgboost (eXtreme Gradient Boosting) boosted tree, input is Dan Yin
The result of prime model and the result of Multiple-Factor Model and the meteorological condition at current time, specifically unifactor model is predicted
It as a result is p={ p1,p2…p6, the result of Multiple-Factor Model is z={ z1,z2…z6, p and z represent PM2.5, PM10, NO2,
CO, O3And SO2Six kinds of concentration datas, the meteorological condition g={ g at current time1, g2..., g6, g represents the temperature at nearest moment,
Air pressure, humidity, wind direction, wind speed, six kinds of data of weather index, in training or prediction, the data that are inputted in xgboost boosted tree
For xi={ pi,zi, g }, i is any one in 1~6, the y in trainingiIt is the current air mass data of mark, including
PM2.5, PM10, NO2, CO, O3, SO2, and the y in test or predictioniThe air quality data as predicted, such as x1=
{ pm2.5 (unifactor model result), pm2.5 (Multiple-Factor Model result), temperature, air pressure, humidity, wind direction, wind speed, weather refer to
Mark (weather conditions at six nearest moment), y1={ pm2.5 (current value in training set) }, xiAnd yiAnd so on.xgboost
Current meteorological condition is added in input data and is trained for boosted tree, and the weight of air quality data is adjusted flexibly, thus
Obtain accurate predicted value.
The method that Xgboost return calculating is as follows:
If there is k tree in xgboost,
What F was indicated in above formula is all function spaces returned in forest,For model predication value, fkWhat is indicated is i-th
Predicted value of the sample in kth tree.
The parameter required now is exactly the structure of each tree and the weight of every leaf, is in simple terms exactly to seek each subtree
fk, wherein setting:
Θ={ f1,f2,...fk}
However objective function obj () is to find a relatively good parameter Θ, formula is as follows:
Obj (Θ)=L (Θ)+Ω (Θ)
Wherein, L (Θ) is the fit solution that error function is used to express data, and Ω (Θ) is regularization term, for punishing
Complex model.
The step of training pattern is that each tree is cumulative, and until k tree stops, process is as follows:
Wherein,After representing i-th circulation, xiPredicted value, t indicate t wheel model training.
Error in training is as follows:
Wherein yiAs corresponding labeled data,When the data predicted, l indicates loss function, carries out costing bio disturbance, often
That sees has Squared Error Loss and Logistic loss.
Penalty term is as follows when training:
It finally obtains with Taylor's formula that the formula of objective function abbreviation is as follows:
Wherein setC is constant,
ft(x)=wq(x),w∈RT,q:Rd→ { 1,2 ..., T }, f (x) indicate the node predicted value of every stalk tree, and w is indicated
The weight of leaf, q indicate the structure of tree, and T indicates the leaf number of tree, RdIndicate that number of features is the data set of d.
Further switch to the minimum value for seeking above formula quadratic function, it may be assumed that
Wherein calculate information gain (Gain) formula are as follows:
Wherein L indicates that left subtree, R indicate right subtree, HLIndicate left subtree weight, HRIndicate right subtree weight, γ indicates to add
Enter the complexity cost that new leaf node introduces.
In this way by after training, xgboost boosted tree is divided by information gain (Gain), each subtree is established
ft(x)=wq(x), each data characteristics, i.e. xi={ pi, zi, g1, g2, g3, g4, g5, g6 } and it include single factor test and Multiple-Factor Model
The air quality data p obtainediAnd ziIt with current every meteorological data g, is mapped on the leaf node of each tree, according to more wheels
Training, the leaf node of each subtree can be adjusted according to weight w, and whole subtrees can also be updated at this time, every height
Tree node is a predicted value, and the foundation for adjusting weight is to obtain optimal value w according to objective function obj, finally all each
A subtree fkPredicted value add up to get the air quality data finally predicted is gone out.
In conclusion the above is merely preferred embodiments of the present invention, being not intended to limit the scope of the present invention.
All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in of the invention
Within protection scope.
Claims (7)
1. a kind of Air Quality Forecast method of the deep learning of multi-model fusion, which comprises the following steps:
Step 1: obtaining history air quality data and meteorological data；
Step 2: carrying out missing values interpolation and normalized to history air quality data；
Step 3: predicting mould using history air quality data deep learning model of the building based on seq2seq as single factor test
Type；
Step 4: constructing the seq2seq depth based on double attention mechanism using history air quality data and meteorological data
Model is practised as multi-factor Estimation Model；
Step 5: history air quality data input single factor test prediction model is obtained into the prediction result of single factor test prediction model,
History air quality data and meteorological data input multi-factor Estimation Model are obtained into the prediction result of multi-factor Estimation Model, it will
The prediction result of single factor test prediction model, the prediction result of multi-factor Estimation Model and current weather data are dissolved into
Recurrence calculating is carried out in xgboost boosted tree, obtains the predicted value of final air quality data.
2. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as described in claim 1, which is characterized in that
Air quality data includes PM2.5, PM10, NO2、CO、O3And SO2Concentration datas more than middle a kind of or two classes.
3. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as claimed in claim 2, which is characterized in that
When air quality data is the concentration data of two classes or more, in step 5, one kind in air quality data is successively predicted, it will
The prediction result of single factor test prediction model and the prediction result of multi-factor Estimation Model of such air quality data and current
Meteorological data, which is dissolved into xgboost boosted tree, carries out recurrence calculating, obtains the predicted value of such air quality data.
4. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as described in claim 1, which is characterized in that
Meteorological data includes temperature, air pressure, humidity, wind direction, wind speed and weather index.
5. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as described in claim 1, which is characterized in that
Missing values interpolation is carried out using desired maximization approach.
6. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as described in claim 1, which is characterized in that
The input layer of single factor test prediction model is history air quality data, and for the structure of hidden layer to be seq2seq model, inside is to compile
Code and decoding structure, coding and decoding unit therein are LSTM long Memory Neural Networks in short-term, and output layer is that air quality is pre-
Measured value.
7. a kind of Air Quality Forecast method of the deep learning of multi-model fusion as described in claim 1, which is characterized in that
The input layer of multi-factor Estimation Model is history air quality data and meteorological data, and hidden layer structure is a kind of double attention machines
The seq2seq model of system, inside be coding-decoding structure, one layer of attention mechanism is separately added into before coding and decoding,
Output layer is air quality data predicted value.
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