Air quality prediction method based on deep neural network

Abstract

The invention relates to an air quality prediction method based on a deep neural network. The method comprises steps: S1, the concentration of each air pollutant at each time point at different locations is acquired; S2, the concentration of each air pollutant is ranked to acquire a concentration vector; S3, the concentration vectors at the same time point at different locations are used as a datagroup, and all data groups in a time sequence are used as a data set; S4, a known set containing the concentration values of all pollutants is selected from multiple data sets, a pollutant concentration data group which is missing in a to-be-detected set is found out, and a corresponding time point is determined; S5, a data group corresponding to the time point is selected from the known set as input data, the rest data groups are used as output data, and input vectors are acquired; and S6, a deep neural network model is built, other data groups in the to-be-detected set are used as input values to be inputted to the model to acquire output values as prediction values. Compared with the prior art, the method has the advantage of improving the prediction precision.

Description

technical field [0001] The invention relates to the technical field of air quality prediction, in particular to an air quality prediction method based on a deep neural network. Background technique [0002] Due to the development of global social economy and industrialization process, as well as the rapid development of world urbanization, the expansion of energy and transportation scale, the expansion of urban population, and the establishment of large industrial development zones have brought unprecedented pressure on the atmospheric environment. The problem of atmospheric environmental pollution with particulate matter, sulfur dioxide, and nitrogen oxides as the main pollutants is becoming more and more serious, and the huge impact on resources and the environment threatens the foundation of sustainable development. my country is a developing country with an unreasonable energy structure, which makes urban air pollution worse. Therefore, how to prevent and control air po...

AI Technical Summary

Problems solved by technology

Although the statistical prediction methods commonly used in the field of air quality prediction in the past are simple to establish, convenient to operate, and easy to popularize, they lack a solid physical foundation and require a large amount of monitoring data; although numerical prediction has a solid physical foundation and comprehensive prediction results, the model The required boundary and initial conditions are not easy to give, the difficulty is high, the calculation time is long, and the accuracy of prediction results is not high

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