CN111047012A - Air quality prediction method based on deep bidirectional long-short term memory network - Google Patents
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- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 description 2
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- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
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Abstract
An air quality prediction method based on a deep bidirectional long-short term memory network. The invention designs a high-precision prediction algorithm aiming at the current situation that air pollutants are difficult to predict. The algorithm innovatively applies a deep bidirectional long-short term memory network to process the time sequence data of the historical pollutant indexes, so that the pollutant is predicted and analyzed. The algorithm was built by the Keras tool of PYTHON3.6.5, comprising the following steps: s1, dividing time series data of a plurality of pollutant indexes into a training set, a verification set and a test set; s2, inputting the data of the training set into a deep bidirectional long and short term memory network for training until the network converges; s3, inputting the data of the verification set into the network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters; s4, applying the network to the test set to evaluate the model, and obtaining the effect of high accuracy; s5, model saving is applied to the actual situation. The algorithm provides a new solution for air pollutant prediction, and is widely applied to the field of air pollution prediction.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to an air quality prediction method based on a deep bidirectional long-short term memory network, and belongs to the field of air pollution prediction.
[ background of the invention ]
The prediction of the concentration of the air pollutants has strong disciplinary crossability and is always a hotspot of researches in the fields of environment, weather, mathematics, geography and computer science. Due to the diversity and complexity of pollution components, the pollution indexes are directly and often in a high nonlinear relation, a traditional mathematical model method is difficult to establish an accurate prediction model, a large amount of data acquisition and analysis of a movement mechanism are needed, and the difficulty degree of air pollutant prediction is increased due to the interference of real-time change of meteorological conditions.
Common methods are largely divided into theoretical methods and statistical-based methods. Statistical-based prediction methods are increasingly gaining attention because they do not require excessive prior knowledge and can efficiently and accurately obtain mapping relationships between inputs and outputs, such as artificial neural networks, clustering, regression, and the like. Currently, long and short term memory networks have been used for air pollution prediction and have achieved good results. Because the forward and backward information can be fully utilized, the accuracy of the bidirectional LSTM is generally higher than that of the unidirectional LSTM, and the stability is better. Therefore, the bidirectional long-short term memory network is used as an excellent deep learning time series prediction method, and hidden information of the forward sequence time series and the reverse sequence time series can be effectively mined and utilized for air pollutant prediction. In recent years, deep learning has shown great advantages in regression tasks, and with this, the applicant proposes an air quality prediction method based on a deep bidirectional long-short term memory network to solve the above problems, so as to improve the accuracy of pollutant prediction.
[ summary of the invention ]
Aiming at the defects in the prior art, the invention designs an air quality prediction method based on a deep bidirectional long-short term memory network. The technical system disclosed by the invention can well utilize the historical pollutant index data to realize the prediction of air quality, and is beneficial to the prevention and treatment of air pollution.
In order to solve the problem of gradient disappearance of the recurrent neural network and fully utilize the relation between the data preorder and the data postorder, the method provided by the patent uses a bidirectional long-short term memory network. The long-short term memory network is one kind of recurrent neural network, and each unit includes input gate, forgetting gate and output gate, and can process the long-short term time sequence data effectively and obtain useful information. The bidirectional circulation neural network increases node connection between each layer of the network, the input of the cell state of the forward layer comprises input and output at the last moment, the input of the cell state of the backward layer comprises input and output at the next moment, and the final output layer is jointly determined by the outputs of the forward layer and the backward layer.
The specific steps of the structure of the whole algorithm are as follows:
s1: preprocessing is carried out after data acquisition to obtain pollutant time sequence data, and a data set is divided into a training set, a verification set and a test set;
s2: inputting data of a training set into a deep bidirectional long and short term memory network for training until the network converges;
the calculation process of the bidirectional long-short term memory network is as follows:
hi＝f(w1xi+w2hi-1)
hi'＝f(w3xi+w5hi+1')
oi＝g(w5hi+w6hi')
where w is the different weights, h is the forward hidden state, h' is the reverse hidden state, x is the input, and o is the output.
S3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
the parameters that need to be adjusted in this patent include the Dropout value, which determines the number of hidden neurons that are deleted randomly, thus preventing the model from being over-fitted.
S4: storing the final model, and inputting a test set to perform recognition effect test;
s5: and the final model is used for an actual air quality prediction link.
The patent uses Mean Square Error (MSE) as a loss function,
wherein y isiIs the true contaminant concentration value, ypiIs the predicted contaminant concentration value.
The indexes of the measurement algorithm are RMSE (root mean square error), MAPE (maximum amplitude error), average percentage error, MAE (maximum amplitude error), average absolute error and goodness of fit R2。
[ description of the drawings ]
FIG. 1 is a flow chart of an algorithm
FIG. 2 is a diagram of a bidirectional long and short term memory network
FIG. 3 is a graph showing the predicted results of carbon monoxide
FIG. 4 is a graph showing the predicted results of nitrogen dioxide
[ detailed description ] embodiments
Fig. 1 shows a flow chart of the algorithm, and fig. 2 shows a structure of the bidirectional long-short term memory network. The algorithm comprises the following specific steps:
step 1: preprocessing is carried out after data acquisition to obtain pollutant time sequence data, and a data set is divided into a training set, a verification set and a test set;
step 2: inputting data of a training set into a deep bidirectional long and short term memory network for training until the network converges;
and step 3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
and 4, step 4: and storing the final model, inputting the test set for recognition effect test, and using the final model for an actual air quality prediction link.
The step 1 comprises the following steps:
step 1.1: data acquisition: the implementation method of the patent comprises the following steps of: historical time series data of different pollutant indicators at the same location, e.g. CO, NO2Etc., the pollutant concentration vector at each moment is T ═ Ct(A),Ct(B),Ct(C),Ct(D),Ct(E)...]Each value in the vector represents a concentration value of a different pollutant at time t;
step 1.2: data preprocessing: and (4) carrying out mean value missing value filling on the index historical time sequence data, and processing and standardizing abnormal values to obtain a final historical time sequence data set.
Step 1.3: data set partitioning: the historical time series data set is divided into a training set, a validation set and a test set in a ratio of 7:2: 1.
The step 2 comprises the following steps:
step 2.1: and constructing a bidirectional long-short term memory network, which comprises an input layer, a forward layer, a backward layer, a full connection layer and an output layer. Wherein, the parameters to be adjusted are the number of the long-term and short-term memory nerve units of each layer and the Dropout value;
step 2.2: initializing the cell state and the hidden state of a bidirectional long-short term memory neural unit, and inputting data;
step 2.3: calculating the weights of an input gate, a forgetting gate and an output gate of the current neuron and the current memory candidate value;
step 2.4: calculating the hidden state and the memory state of the current neuron and transmitting the hidden state and the memory state to the next neuron;
step 2.5: the above steps are trained and cycled through using MAE as a loss function until the model converges.
The step 3 comprises the following steps:
step 3.1: testing the trained model on the verification set;
step 3.2: adjusting parameters according to the model result;
step 3.3: and (5) completing parameter adjustment and saving the model.
The step 4 comprises the following steps:
step 4.1: finally training a test set to obtain a well-trained prediction model;
step 4.2: comparing all the prediction results with the true values to obtain a final model evaluation index;
step 4.3: the final prediction model can be used in the actual air quality monitoring link.
The evaluation result of the final model is shown in table 1, and the prediction trend graph is shown in fig. 3 and fig. 4, so that the algorithm in the patent has good practical prospect and generalization capability.
TABLE 1 prediction results table
It should be noted that the above examples are only used for illustrating the patent pollutant prediction description of the present invention, and are not intended to limit the present invention. It should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be included within the scope of the claims of the present invention.
Claims (6)
1. The air quality prediction method based on the deep bidirectional long and short term memory network comprises the following steps:
step 1: preprocessing is carried out after data acquisition to obtain pollutant time sequence data, and a data set is divided into a training set, a verification set and a test set;
step 2: inputting data of a training set into a deep bidirectional long and short term memory network for training until the network converges;
and step 3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
and 4, step 4: and storing the final model, inputting the test set for recognition effect test, and using the final model for an actual air quality prediction link.
2. The method of deep two-way long-short term memory network in air pollutant prediction as claimed in claim 1, its application range in air pollutant and indoor pollutant prediction system can effectively predict the development of pollutant index.
3. The method for predicting the air quality based on the deep bidirectional long and short term memory network as claimed in claim, wherein the step 1 comprises the following steps;
step 1.1: data acquisition: the implementation method of the patent comprises the following steps of: historical time series data of different pollutant indicators at the same location, e.g. CO, NO2Etc., the pollutant concentration vector at each moment is T ═ Ct(A),Ct(B),Ct(C),Ct(D),Ct(E)...]Each value in the vector represents a concentration value of a different pollutant at time t;
step 1.2: data preprocessing: and (4) carrying out mean value missing value filling on the index historical time sequence data, and processing and standardizing abnormal values to obtain a final historical time sequence data set.
Step 1.3: data set partitioning: the historical time series data set is divided into a training set, a validation set and a test set in a ratio of 7:2: 1.
4. The method for predicting the air quality based on the deep bidirectional long and short term memory network as claimed in claim, wherein the step 2 comprises the following steps;
step 2.1: and constructing a bidirectional long-short term memory network, which comprises an input layer, a forward layer, a backward layer, a full connection layer and an output layer. Wherein, the parameters to be adjusted are the number of the long-term and short-term memory nerve units of each layer and the Dropout value;
step 2.2: initializing the cell state and the hidden state of a bidirectional long-short term memory neural unit, and inputting data;
step 2.3: calculating the weights of an input gate, a forgetting gate and an output gate of the current neuron and the current memory candidate value;
step 2.4: calculating the hidden state and the memory state of the current neuron and transmitting the hidden state and the memory state to the next neuron;
step 2.5: the above steps are trained and cycled through using MAE as a loss function until the model converges.
5. The method for predicting the air quality based on the deep bidirectional long and short term memory network as claimed in claim, wherein the step 3 comprises the following steps;
step 3.1: testing the trained model on the verification set;
step 3.2: adjusting parameters according to the model result;
step 3.3: and (5) completing parameter adjustment and saving the model.
6. The method for predicting the air quality based on the deep bidirectional long and short term memory network as claimed in claim, wherein the step 4 comprises the following steps;
step 4.1: finally training a test set to obtain a well-trained prediction model;
step 4.2: comparing all the prediction results with the true values to obtain a final model evaluation index;
step 4.3: the final prediction model can be used in the actual air quality monitoring link.
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