Air quality prediction method

A technology of air quality and normalization, applied in instruments, character and pattern recognition, computer components, etc., can solve the problem of subjectivity in hidden state numbers of HMM model

Inactive Publication Date: 2017-05-31
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a method for predicting air quality, so as to solve the subjective problem of artificially determining the hidden state number of the HMM model existing in the prior art

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Embodiment Construction

[0070] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0071] The invention provides a method for predicting air quality aiming at the subjectivity problem of the existing artificial determination of the hidden state number of the HMM model.

[0072] see figure 1 As shown, the method for predicting air quality provided by the embodiments of the present invention includes:

[0073] Step 101, obtaining an observation sequence;

[0074] Step 102, using the FCM clustering algorithm to cluster the acquired observation sequence to obtain the optimal number of clusters and corresponding clustering results;

[0075] Step 103, according to the clustering results, establish an HMM model for each class, and determine the optimal number of clusters for each class again through the FCM clustering algorithm;

[0076] S...

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Abstract

The invention provides an air quality prediction method, which can automatically determine a hidden state number of an HMM. The method comprises the steps of obtaining observation sequences; clustering the obtained observation sequences by utilizing an FCM clustering algorithm to obtain an optimal clustering number and a corresponding clustering result; building the HMM for each category according to the clustering result, and determining an optimal clustering number of each category through the FCM clustering algorithm; taking the determined optimal clustering number of each category as the hidden state number of the built HMM; and training the built HMM according to the clustering result, and based on the trained HMM, predicting air quality. The method is suitable for the technical field of environmental detection.

Description

technical field [0001] The invention relates to the technical field of environmental detection, in particular to a method for predicting air quality. Background technique [0002] The quality of air quality is determined by pollution sources on the one hand, and local meteorological factors on the other hand. In the case of the same pollution source, the concentration of ground pollutants caused by different meteorological factors varies greatly. It can be seen that air quality is closely related to meteorological factors. In order to improve regional air quality, it is becoming more and more important to study the relationship between regional air quality time series and meteorological factors. [0003] In recent years, many forecasting methods have been proposed at home and abroad for the characteristics of nonlinearity, randomness, timing, dynamics and uncertainty of the air quality time series, among which the Hidden Markov Model (HMM) forecasting method It is one of t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23211G06F18/214
Inventor 王玲肖希元孟建瑶
Owner UNIV OF SCI & TECH BEIJING
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