Air pollutant concentration prediction method

Abstract

The invention discloses an air pollutant concentration prediction method. The method includes the steps that multi-scale wavelet decomposition is performed on air pollutant data through a Mallat algorithm, a final-level-scale low-frequency approximating sequence uses a support vector regression (SVR) model for modeling prediction, other high-frequency detailed sequences use an auto-regression movement average (ARMA) model for modeling prediction, various levels of coefficient sequences are reconstructed through the Mallat algorithm, and an air pollutant concentration prediction result is obtained. Different models are used for sequences of different levels of scales obtained after wavelet decomposition according to the application characteristics of different models, and SVR modeling prediction is used for the final-level-scale relatively-unstable low-frequency approximating sequence and ARMR modeling prediction is used for other stable high-frequency detailed sequences on the basis of the characteristic that ARMA is more suitable for prediction of stable sequences and SVR is more suitable for prediction of unstable sequences. The method can achieve high prediction precision.

Description

technical field [0001] The invention relates to a data prediction method, in particular to an air pollutant concentration prediction method. Background technique [0002] At present, the commonly used urban air pollutant concentration forecasting methods mainly include numerical forecasting, stepwise linear regression model, gray forecasting, autoregressive moving average model (AutoRegressiveandMovingAverage, ARMA), support vector regression model (SupportVectorMachine, SVR), support vector regression model, Artificial neural network models, etc. Among them, autoregressive moving average model is an effective tool for modeling stationary time series, and has been widely used in urban air pollutant concentration forecasting. [0003] Due to the influence of various meteorological conditions and atmospheric physical and chemical processes, the concentration of air pollutants will appear in various abnormal situations. However, the existing autoregressive moving average model...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art, and provide a method for predicting the concentration of air pollutants to solve the problem that the accuracy of predicting the concentration of air pollutants in the prior art is not high

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