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Method and system for predicting nitrogen dioxide concentration

A nitrogen dioxide and concentration prediction technology, applied in prediction, design optimization/simulation, instruments, etc., can solve problems such as few prediction methods, no consideration of spatial factors, poor portability, etc., to reduce prediction residuals, coverage Wide range, guaranteed transplantable effect

Active Publication Date: 2022-07-05
SHANDONG UNIV
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AI Technical Summary

Problems solved by technology

Although there are many methods and applications of machine learning algorithms for air quality prediction, there are few methods for predicting atmospheric nitrogen dioxide concentrations in large-scale regions
The machine learning prediction method based on a small-scale, single data source (pollution data and meteorological data) does not consider the influence of spatial factors in different regions, has poor portability, and is only suitable for air quality prediction in the current region

Method used

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  • Method and system for predicting nitrogen dioxide concentration
  • Method and system for predicting nitrogen dioxide concentration

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Embodiment 1

[0035] The present embodiment 1 provides a nitrogen dioxide concentration prediction system, and the system includes:

[0036] an acquisition module for acquiring monitoring data; the monitoring data includes air pollution monitoring data, meteorological data, remote sensing reanalysis meteorological field data and geographic covariate data;

[0037] The prediction module is used to separately process the monitoring data using a pre-trained random forest model, an extreme gradient boosting tree model and a gated recurrent unit neural network model combined with residual connections to obtain three predicted values ​​of nitrogen dioxide concentration ;

[0038] The calculation module is used for calculating the final nitrogen dioxide concentration value based on the three predicted values ​​of nitrogen dioxide concentration combined with the weighted average algorithm.

[0039] In the present embodiment 1, the nitrogen dioxide concentration prediction method is realized by usi...

Embodiment 2

[0057] In this embodiment 2, a method based on machine learning is provided to predict the short-term NO of an air quality monitoring station in a certain area. 2 Concentration method to achieve large-scale, multi-sequence NO 2 The rapid and accurate prediction of concentration solves the problem that the current machine learning prediction method has low portability and cannot be applied to new monitoring stations with little historical data.

[0058] In this Example 2, the short-term NO of the air quality monitoring station is predicted based on machine learning. 2 Concentration method, the implementation process specifically includes the following steps: step 1, obtaining air pollution monitoring data and auxiliary feature data sets covering a certain area, and obtaining multi-source data sets; step 2, performing multi-source data sets for long time series Preprocessing, time and space fusion, using resampling technology to generate data sets with different time resolution...

Embodiment 3

[0077] In this embodiment 3, a method based on machine learning is provided to predict the short-term NO of an air quality monitoring station 2 Concentration method, the implementation method specifically includes the following steps: step 1, obtaining air pollution monitoring data and auxiliary feature data sets covering the target area; step 2, performing preprocessing, time and space fusion on long-sequence multi-source data sets, Use resampling technology to generate data sets with different temporal resolutions, step 3, based on the fused multi-source data set, use feature engineering to extract spatiotemporal information and add it to the data set and divide the training set and test set; step 4, train the multi-source data set based on machine learning Timing NO 2 The model of the relationship with the feature vector, and finally realize the NO of multiple time series in the target area 2 Concentration prediction.

[0078] The specific calculation is to use z-score no...

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Abstract

The invention provides a nitrogen dioxide concentration prediction method and system, belonging to the technical field of air quality monitoring, and obtains monitoring data such as air pollution monitoring data, meteorological monitoring data, remote sensing reanalysis meteorological field data and geographic covariate data; The forest model, the extreme gradient boosting tree model, and the gated recurrent unit neural network model combined with residual connection process the monitoring data respectively to obtain three predicted values ​​of nitrogen dioxide concentration, and then combine with the weighted average algorithm to calculate the final nitrogen dioxide concentration. Nitrogen concentration value. The invention integrates multi-source spatiotemporal data, and learns the temporal and spatial variation patterns of nitrogen dioxide; through integrated learning combined with the advantages of different algorithms, the stability of the prediction results is improved, the prediction residual error is reduced, and the coverage is wide and the prediction is realized. High-precision, multi-time series nitrogen dioxide concentration prediction; ensures the portability of the machine learning prediction method, and can be directly applied to new monitoring stations with little historical data.

Description

technical field [0001] The invention relates to the technical field of air quality monitoring, in particular to a method and system for predicting nitrogen dioxide concentration based on a machine learning algorithm. Background technique [0002] Excessive use of coal, oil, natural gas and other fossil fuels has made the air pollution problem more and more serious, causing a series of impacts on people's life and health. Long-term exposure to air pollution can cause respiratory system, cardiovascular and other diseases, and even cause death . Therefore, we should attach great importance to the prevention and control of air pollution, and continue to promote the refined and scientific prevention and control of the atmospheric environment. Timely prediction and early warning of air pollutant concentrations can remind people to do preventive work in advance, and help decision makers to propose solutions in time to avoid problems. , curb the impact of air pollution. [0003] T...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N20/20G06Q10/04G06Q50/26
CPCY02A90/10
Inventor 张庆竹汪先锋陶辰亮王桥王文兴
Owner SHANDONG UNIV
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