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PM2.5 concentration prediction method and system for optimizing RELM based on random forest and ISCA

A random forest and concentration prediction technology, applied in prediction, computer parts, character and pattern recognition, etc., can solve the problems of low accuracy, high cost, poor PM2.5 concentration prediction ability, etc., to improve accuracy and predict cost The effect of low, excellent prediction accuracy

Pending Publication Date: 2021-09-03
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0004] Purpose of the invention: In order to solve the above technical problems, the present invention provides a PM2.5 concentration prediction method and system based on random forest and ISCA optimized RELM, which overcomes the problems of poor PM2.5 concentration prediction ability, high cost and low precision at the present stage. shortcoming

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  • PM2.5 concentration prediction method and system for optimizing RELM based on random forest and ISCA
  • PM2.5 concentration prediction method and system for optimizing RELM based on random forest and ISCA
  • PM2.5 concentration prediction method and system for optimizing RELM based on random forest and ISCA

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[0051] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0052] The present invention provides a PM2.5 concentration prediction method based on random forest and ISCA optimization RELM, such as figure 1 As shown, the specific steps are as follows:

[0053] Step 1: Obtain historical PM2.5 concentration data within a preset time range, preprocess the obtained PM2.5 concentration data, and obtain a training set and a test set.

[0054] The present invention uses the PM2.5 concentration data from 0:00 to 22:00 every day for 30 days from July 1 to July 30, 2020 at the Nanjing Gaochun Chunxi monitoring point as a data set, and pre-sets the PM2.5 concentration data. Processing to obtain training set and test set; the training set accounts for 70% of the total data set, and the test set accounts for 30%. Determine whether there is a sudden change point in the PM2.5 concentration in the training set. The sudden change poi...

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Abstract

The invention discloses a PM2.5 concentration prediction method and system for optimizing RELM based on random forest and ISCA, and the method comprises the steps: (1) obtaining historical PM2.5 concentration data in a preset time range, carrying out the preprocessing of the PM2.5 concentration data, and obtaining a training set and a prediction set; (2) carrying out feature selection on the data by using a random forest algorithm to ensure the prediction precision; (3) training the processed PM2.5 concentration data by using a regularization extreme learning machine; (4) improving a sine and cosine algorithm by using a uniform algorithm, nonlinearity and chaos search; and (5) establishing a model for optimizing an extreme learning machine (RELM) based on an improved sine and cosine algorithm (ISCA). Compared with a traditional prediction model, the method and system show more excellent prediction precision, and can further improve the accuracy of PM2.5 concentration prediction.

Description

technical field [0001] The invention belongs to the technical field of PM2.5 concentration prediction, in particular to a PM2.5 concentration prediction method and system based on random forest and ISCA optimized RELM. Background technique [0002] In recent years, with the rapid development of my country's economy and technology, domestic environmental pollution has become more serious, and air pollution and haze incidents have occurred frequently, especially in densely populated, economically developed, and energy-consuming areas such as North China and the Yangtze River Delta. Toxic gases emitted by factories, automobiles, etc. will enter the atmosphere, causing environmental problems such as acid rain and smog. These harmful substances include PM2.5. PM2.5 refers to particulate matter with a diameter less than or equal to 2.5 microns in the ambient air. It can be suspended in the air for a long time. If the concentration is greater, the air pollution is more serious. Thi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06N3/00
CPCG06Q10/04G06N3/006G06F18/2113G06F18/24323G06F18/214
Inventor 马慧心张楚彭甜王业琴赵环宇张大鹏钱程
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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