CN110852493A - Atmospheric PM2.5 concentration prediction method based on multiple model comparisons - Google Patents
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Abstract
The invention belongs to the field of atmospheric pollution prediction model construction, and particularly relates to an atmospheric PM2.5 concentration prediction method based on multiple model comparisons, which comprises the following steps: (1) collecting pollutant concentration data and meteorological data of a target city; (2) preprocessing the acquired pollutant concentration data and meteorological data; (3) respectively constructing a PM 2.5-multivariate linear regression model, a BP neural network model and a time series model aiming at the acquired pollutant concentration data and meteorological data; (4) comparing all parameters of the three models and determining the model with the highest accuracy; (5) and establishing a winter PM2.5-BP neural network model based on seasons.
Description
Technical Field
The invention belongs to the field of atmospheric pollution prediction model construction, and relates to an atmospheric PM2.5 concentration prediction method based on multiple model comparisons.
Background
With the rapid development of the world industry and economy, countries in the world are still making scientific and technical innovations and breakthroughs in production, and meanwhile, various resource and environmental problems are left. In recent years, various atmospheric environmental problems such as greenhouse effect, acid rain and frequent haze have great influence on the atmospheric environment in the world nowadays. In a country with 14 billion population, the gradual deterioration of the atmospheric environment causes various social problems, such as damage to historical cultural relics and buildings, occurrence of various respiratory diseases, infection of epidemic diseases and the like. The Beichen area of Tianjin City, which is located in the area with high atmospheric environmental pollution in China, Jingjin Ji area, also suffers from huge environmental problems, and needs to be solved urgently. At present, the research on the atmospheric pollutants in China has been widely paid attention to by the public and governments, and many atmospheric environment monitoring sites are also established to monitor the data of the atmospheric pollutants in real time, however, the data of the atmospheric pollutants in China is not fully utilized so far. Aiming at the increasingly serious haze condition, an effective PM2.5 prediction system is built at present.
Disclosure of Invention
The invention aims to provide an atmospheric PM2.5 concentration prediction method based on multiple model comparisons, and by using the method, the PM2.5 condition of a target city can be well predicted.
The technical scheme provided by the invention is an atmospheric PM2.5 concentration prediction method based on multiple model comparisons, which comprises the following steps:
(1) collecting concentration data and meteorological data of various atmospheric conventional pollutants of a target city, wherein the atmospheric conventional pollutant data mainly comprises PM2.5 and O3、PM10、SO2、CO、NO2The meteorological data mainly comprises temperature, wind power and weather, and after the data are acquired, the data are processedEstablishing a database;
(2) reading concentration data and meteorological data of various atmospheric conventional pollutants in a target city from a database, and preprocessing the data;
(3) establishing a multivariate linear regression model, a BP neural network model and a time series model for PM2.5 of a target city;
(4) determining an index system for evaluating the performance of each model, and finally determining a model with the highest prediction accuracy in the models through comprehensive comparison of each index;
(5) on the basis of the condition that the PM2.5 is too high in winter possibly occurring in a target city, establishing a winter database and preprocessing data for various atmospheric conventional pollutant concentration data and meteorological data in winter, and establishing a prediction model for the PM2.5 in winter by using the determined model with the highest prediction precision.
In the step (1), the concentration data and meteorological data of various atmospheric conventional pollutants in the target city come from the local environmental protection bureau and meteorological bureau of the target city.
In the step (1), Excel software is used for establishing the database.
In step (2), the preprocessing method for the data of the multiple linear regression model and the time series comprises the steps of using Excel to carry out consistency check on the obtained data, replacing or reasonably processing invalid values and missing values and using normalization to [0, 1]]The method of (3) normalizes the data, and the formula is as follows:
to obtain the true predicted value, we can use the formula nextAnd the prediction value required by us is converted back.
In the step (2) of the method, for the data preprocessing of the BP neural network model, the steps of inserting preprocessing function codes into Python to carry out consistency check, replacing invalid values and missing values or reasonably processing the invalid values and missing values and using normalization to [0, 1] are adopted, and meanwhile, random rearrangement is carried out on the data by utilizing a reindex function.
In the step (3), the data used by the method is all atmospheric conventional pollutant concentration data and meteorological data in the time selected by the target city, software SPSS is used for constructing a multiple linear regression model, and a stepwise regression method and a step-by-step regression method F are usedStatistics>FINThe removal conditions of (1).
In the step (3), the time series model is constructed by using the SPSS software, and the ARIMA model with the structure (p, d, q) is adopted.
In the step (3), a BP neural network model is constructed based on Python, and the structure is as follows: each sample (batch size) entered the model was scaled to the training, test and validation sets to 6: 2: 2, adopting a PM2.5-BP neural network structure with a single hidden layer, wherein the structure is 8-4-1, adopting two types of activation functions of ReLU and Purelin in a hidden layer and an output layer of the neural network respectively, and designing the maximum iteration times and the learning rate according to actual conditions.
In the step (4) of the method, the selected evaluation indexes are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R)2) The formulas are respectively the following formulas, and the evaluation of each model comprehensively considers the numerical value of each index to determine the finally selected model.
In the step (5), the modeling method of the winter PM 2.5-optimal model of the target city is the same as the modeling methods of the three models in the step (5), and the finally used model depends on the finally selected model.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is the comparison statistics of the performance of each index of the three models.
In fig. 3, (a), (b), and (c) are PM2.5 actual measurement-prediction scattergrams when a multiple linear regression model, a BP neural network model, and a time series model are used, respectively.
FIG. 4 is a comparison of indexes of the PM2.5-BP neural network model and the winter PM2.5-BP neural network model.
Detailed Description
The present invention will be described below with reference to specific examples, but the present invention is not limited thereto.
Taking the northen district of Tianjin City as an example for modeling, PM2.5 of the northen district of Tianjin City from 6 months 1 days in 2018 to 5 months 31 days in 2019 is modeled by applying the model.
The concentration data and meteorological data of various atmospheric conventional pollutants are provided by an ecological environment monitoring center in the North-Chen district in Tianjin and a meteorological office website.
1. Data collection and database creation
Collecting concentration data and meteorological data of various atmospheric conventional pollutants in the North-Chen district of Tianjin City, wherein the atmospheric conventional pollutant data mainly comprises PM2.5 and O3、PM10、SO2、CO、NO2The meteorological data mainly comprises temperature, wind power and weather.
After the data are acquired, establishing a database for each item of data by using Excel.
2. Pre-processing of data
Reading from a databaseThe method comprises the steps of obtaining concentration data and meteorological data of various atmospheric conventional pollutants in the north-hour area of Tianjin, and preprocessing the data. Methods of data preprocessing for multiple linear regression models and time series include consistency checking of the resulting data using Excel, substitution or other reasonable processing of invalid and missing values, and use of normalization to [0, 1]]The method of (3) normalizes the data, and the formula is as follows:
to obtain the true predicted value, we can use the formula next
And the prediction value required by us is converted back.
For data preprocessing of the BP neural network model, steps of inserting preprocessing function codes into Python to carry out consistency check, substituting invalid values and missing values or carrying out other reasonable processing, using normalization to [0, 1], and simultaneously carrying out random rearrangement on data by utilizing a reindex function are adopted.
3. Establishment of prediction model
The data used for the construction of the prediction model is all atmospheric conventional pollutant concentration data and meteorological data from 6 months 1 days in 2018 to 5 months 31 days in 2019 in the North district of Tianjin City, software SPSS is used for constructing a multiple linear regression model, a stepwise regression method and F are usedStatistics>2.71 rejection conditions.
The time series model was also constructed using the SPSS software and the ARIMA model of structure (14,1,1) was used.
A BP neural network model is constructed based on Python, and the structure is as follows: each sample (batch size) entered the model was set to the ratio of training set, test set and validation set as 201: 68: 68, adopting a PM2.5-BP neural network structure with a single hidden layer, wherein the structure is 8-4-1, adopting two types of activation functions of ReLU and Purelin in a hidden layer and an output layer of the neural network respectively, the maximum iteration number is 6000, and the learning rate is 0.005.
4. Evaluating the performance of each model and determining the optimal model
And (3) evaluating indexes of the selected model performance: mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), and coefficient of determination (R)2) And carrying out comparative evaluation on the performances of the models.
As shown in fig. 2, the final BP neural network data are respectively: 124.90, 11.18, 6.6, 16.37 and 0.92, and as shown in fig. 3(a), (b) and (c), the BP neural network scatter diagram is closer to the trend line, so the BP neural network model is selected as the prediction model used at this time and is used for the model used for the later prediction of the PM2.5 in winter. 5. Construction of winter PM2.5-BP neural network prediction model by using BP neural network model
The ratio of training set, test set and validation set was set to 66: 23: 23, adopting a single-hidden layer winter PM2.5-BP neural network structure, wherein the structure is 8-4-1. In a hidden layer and an output layer of a neural network, two types of activation functions, namely ReLU and Purelin, are respectively adopted, and final parameters are set as follows: the maximum number of iterations is 3000 and the learning rate is 0.003.
By contrast, as shown in fig. 4, the indexes of the prediction model constructed for the PM2.5 in winter are generally better than those of the PM2.5-BP neural network model of the whole year, and the Mean Square Error (MSE), the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R)2) 125.56, 11.21, 2.49, 11.12, 0.97, respectively.
Claims (7)
1. The atmospheric PM2.5 concentration prediction method based on multiple model comparisons is characterized by comprising the following steps:
(1) collecting concentration data and meteorological data of various atmospheric conventional pollutants of a target city, wherein the atmospheric conventional pollutant data mainly comprises PM2.5 and O3、PM10、SO2、CO、NO2The meteorological data mainly comprise temperature, wind power and weather, and after the data are obtained, a database is established for each data;
(2) reading concentration data and meteorological data of various atmospheric conventional pollutants in a target city from a database, and preprocessing the data;
(3) establishing a multivariate linear regression model, a BP neural network model and a time series model for PM2.5 of a target city;
(4) determining an index system for evaluating the performance of each model, and finally determining a model with the highest prediction accuracy in the models through comprehensive comparison of each index;
(5) on the basis of the condition that the PM2.5 is too high in winter possibly occurring in a target city, establishing a winter database and preprocessing data for various atmospheric conventional pollutant concentration data and meteorological data in winter, and establishing a prediction model for the PM2.5 in winter by using the determined model with the highest prediction precision.
2. The method for predicting the concentration of the atmospheric PM2.5 based on the comparison of multiple models as claimed in claim 1, wherein in the step (1), the concentration data and the meteorological data of the atmospheric conventional pollutants in the target city are obtained from the local environmental protection bureau and the meteorological bureau of the target city, and Excel software is used for establishing the database.
3. The method for predicting the concentration of PM2.5 in the atmosphere based on multiple model comparisons according to claim 1, wherein in the step (2), the method for preprocessing the data of the multiple linear regression model and the time series comprises the steps of using Excel to carry out consistency check on the obtained data, replacing or reasonably processing invalid values and missing values and using normalization to [0, 1]The method of (3) normalizes the data, and the formula is as follows:
then, using the formula
And the required predicted value is converted back.
4. The method for predicting the concentration of the atmospheric PM2.5 based on multiple model comparisons according to claim 1, wherein in the step (2), the preprocessing of the data of the BP neural network model adopts the steps of inserting a preprocessing function code into Python to perform consistency check, replacing invalid values and missing values or other reasonable processing, and normalizing to [0, 1] and simultaneously randomly rearranging the data by using a reindex function.
5. The atmospheric PM2.5 concentration prediction method based on multiple model comparisons according to claim 1, characterized by: in the step (3), the used data are all atmospheric conventional pollutant concentration data and meteorological data in the time selected by the target city, a software SPSS is used for constructing a multiple linear regression model, and a stepwise regression method and a F are usedStatistics>FINThe removal conditions of (1).
6. The atmospheric PM2.5 concentration prediction method based on multiple model comparisons according to claim 1, characterized by: in the step (3), a time sequence model is constructed by using SPSS software, and an ARIMA model with a structure of (p, d, q) is adopted; a BP neural network model is constructed based on Python, and the structure is as follows: each sample (batch size) entered the model was scaled to the training, test and validation sets to 6: 2: 2, adopting a PM2.5-BP neural network structure with a single hidden layer, wherein the structure is 8-4-1, adopting two types of activation functions of ReLU and Purelin in a hidden layer and an output layer of the neural network respectively, and designing the maximum iteration times and the learning rate according to actual conditions.
7. The atmospheric PM2.5 concentration prediction method based on multiple model comparisons according to claim 1, characterized by: in the step (4), the selected evaluation indexes are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R)2) The formulas are respectively the following formulas, and the evaluation of each model comprehensively considers the numerical value of each index to determine the finally selected modelType (2):
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