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Air quality prediction method based on iterative learning

A technology of air quality and forecasting methods, which is applied in forecasting, data processing applications, calculations, etc., and can solve problems such as weakening correlation

Inactive Publication Date: 2017-12-01
BEIJING UNIV OF TECH
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  • Summary
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as the time interval increases, this correlation rapidly weakens and declines, so it is difficult to directly apply the meteorological parameters at the current moment to effectively predict the air quality in the next few tens of hours

Method used

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  • Air quality prediction method based on iterative learning
  • Air quality prediction method based on iterative learning
  • Air quality prediction method based on iterative learning

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Experimental program
Comparison scheme
Effect test

Embodiment

[0016] The first step is to collect relevant meteorological data

[0017] The collected meteorological parameters and air pollutant concentrations include: time, temperature (°C), relative humidity (%), wind speed (m / s), air pressure (hPa), visibility (km), AOT, CO (ppm), NO 2 (ppb), O 3 (ppb), PM2.5 (μg / m 3 ). The concentrations of air pollutants that need to be predicted are CO, NO 2 , O 3 , PM2.5.

[0018] The second step is to perform data dimension reduction processing on meteorological data and pollutant concentration data

[0019] In the actual meteorological changes, the changes in the concentration of atmospheric pollutants have highly nonlinear and chaotic characteristics. In order to better predict the air quality, these data need to be linearized. The KPCA method provides a bridge between the data from nonlinear to linear transformation. The KPCA method uses nonlinear mapping to map the original data from the data space to the feature space, and then perfor...

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Abstract

The invention discloses an air quality prediction method based on iterative learning for the first time. It is of great practical significance to predict the air quality in order to prevent and control air pollution in time. The research shows that the change in air quality in a very short time can be estimated through air quality prediction using a direct strategy, but as time goes on, the long-time air quality prediction effect for a few hours later is poor. In order to solve the problem, the invention presents an air quality prediction model based on the iterative idea to predict the hourly air quality at any time in the next 24 hours. The experimental results show that the iteration-based air quality prediction model presented by the invention can be used to well predict the air quality in the next 24 hours, and has an obvious advantage in long-time air quality prediction.

Description

technical field [0001] The invention belongs to a method for predicting air quality, which utilizes an iteration-based method to establish an air quality predicting model, and realizes effective prediction of hour-level air quality for the next 24 hours. Background technique [0002] In recent years, with the rapid development of urbanization and industrialization, the air pollution situation in many areas of our country has become increasingly severe, and the air quality is not optimistic. Common air pollutants are NO 2 , O 3 , CO, PM2.5, etc. These air pollutants can easily cause inflammation of the human respiratory tract, damage the blood circulation and nervous system of the human body, and even cause human death. Therefore, how to effectively prevent human beings from the harm of air pollution has received extensive attention. However, it is very difficult to achieve control of air pollution in the short term. Therefore, by effectively predicting the air quality i...

Claims

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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 乔俊飞顾锞许进超李晓理
Owner BEIJING UNIV OF TECH