Air quality prediction method and system based on data mining

Abstract

The invention discloses an air quality prediction method and system based on data mining. The system is used for achieving the air quality prediction method based on data mining. The method comprisesthe following steps of acquiring a wind speed level, a fine particulate matter pollution value and a local pollutant pollution value, and taking the three pieces of information as the input parameters; standardizing the input parameters; and inputting the standardized input parameters into a BP neural network model to predict the air quality, wherein the local pollutants are selected according tothe main pollutant type of the prediction region, the BP neural network model is of a three-layer design composed of an input layer, a hidden layer and an output layer, and the number of the nodes ofthe hidden layer is 5, 6 or 7. The network model provided by the invention is simple in structure and low in computing resource consumption, can realize the accurate prediction, and also has the characteristics of high convergence speed and strong network generalization capability.

Description

technical field [0001] The invention belongs to the technical field of computers and meteorology, and in particular relates to related technologies of air quality prediction. Background technique [0002] In my country, the main pollutants considered in air quality assessment are fine particulate matter (PM2.5), respirable particulate matter (PM10), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), carbon monoxide (CO), etc. Fine particulate matter (PM2.5) refers to particulate matter with a diameter of 2.5 microns or smaller, while respirable particulate matter (PM10) includes those particles with a diameter of 10 microns or smaller. Accurate air quality prediction is helpful to the prevention and control of air pollution and the planning and construction of urban environment, and can help people reduce unnecessary losses and arrange travel reasonably, which has important guiding significance for people's production and life. [0003] The existing air qual...

AI Technical Summary

Problems solved by technology

Although the existing statistical forecasting methods are simple to establish, convenient to operate, and easy to popularize, they need a large amount of monitoring data to support them; although the physical foundation of numerical forecasting is solid, the degree to which the concentration of pollution sources and meteorological factors affect air quality varies. The input parameters and filter conditions are not easy to give, which makes the accuracy of the prediction results not high

With the development of computer big data related technologies and the increasing improvement of monitoring data, the use of data mining methods to establish air quality prediction models has attracted more and more attention. However, it has disadvantages such as weak ability of transformation, low prediction accuracy and complicated calculation.

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