PM2.5 prediction and early warning method and system based on nonlinear theory

Abstract

The present invention discloses a PM2.5 prediction and early warning method and system based on the nonlinear theory. The method comprises a model training step and a model prediction step, and specifically comprises: dividing PM2.5 concentration time series data into two groups, and taking the two groups as a training time series data set and a test time series data set respectively; performing S-level wavelet decomposition and time-frequency analysis on the data of the training time series data set, expanding one-dimensional information into high-dimensional information, extracting implicitinformation of PM2.5 historical data, and obtaining a training time series index data set; constructing a predictive model; training the predictive model; performing MLRC-LSSVR model prediction on thetest time series data set, performing variance analysis on a model prediction result, and obtaining an upper critical value of the confidence interval as a final prediction result. The technical scheme of the present invention can provide adjustable parameters of the model, and can adapt to the prediction and early warning work of the PM2.5 concentration in different regions by changing the adjustable parameters.

Description

technical field [0001] The invention relates to the field of air quality prediction and early warning, in particular to a PM2.5 prediction and early warning method and system based on nonlinear theory. Background technique [0002] The main component of smog is PM2.5, which is a colloidal mixture of particles with a particle size of less than 2.5 μm. The influencing factors of PM2.5 are complex, and its concentration changes present nonlinear characteristics. [0003] At present, there are mainly two types of air pollutant concentration prediction methods: statistical models and deterministic models. Among them, the statistical model is generally based on historical data to establish a correlation model between air quality and influencing factors. Its advantage is that it has relatively low requirements for input data, but its prediction accuracy is low, it is difficult to reflect regional air quality, and it cannot determine the causes and sources of pollution. The numeri...

AI Technical Summary

Problems solved by technology

The single-step forecast works well, but when performing multi-step forecasts, each step of the forecast requires the output of the last forecast as input. In this iterative process, the last forecast result will affect the forecast result at the next time point , the error will gradually accumulate until the end, and the prediction effect will gradually weaken

[0006] In summary, in the prior art, there is still a lack of an effective solution to the problem of PM2.5 prediction

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