Air quality prediction optimization method and system based on deep learning

Abstract

The invention provides an air quality prediction optimization method and system based on deep learning. According to the method, the deviation between the prediction variable and the actual distribution of the air quality model CMAQ is corrected under the condition of utilizing enough historical data; according to the prediction of atmospheric pollutants by a traditional model and the data of an atmospheric data detection station, a data set to be improved is made, and the traditional model is combined with a deep learning algorithm by using a long-short-term memory network to complete the optimization of air quality prediction. According to the invention, the cascade long-short-term memory C-LSTM network is utilized to better mine long-term sequence characteristics, so that gradient explosion is avoided; an XGBoost network is used for optimizing selection time and other auxiliary factors to remove unimportant or interference features, features such as traditional model prediction andclimate are fully extracted by training the model, and the problem of systematic errors of the traditional model is solved.

Description

technical field [0001] The invention belongs to the technical field of air quality index prediction, and in particular relates to an air quality prediction optimization method and system based on deep learning. Background technique [0002] In recent years, environmental issues have become the focus of people's attention. Various human chronic diseases can be caused by different pollutants in the air, including SO2 (sulfur dioxide), NO2 (nitrogen dioxide), NO (nitrogen monoxide), PM2.5 and PM10, etc. Multiple studies have shown that exposure to highly polluted environments can lead to cardiovascular and respiratory diseases in humans. With the rapid development of industry and the increase of population, air pollution has become a serious problem in western China. Therefore, it is necessary to establish an accurate pollutant prediction and alarm system in urban areas, which plays an important role in people's living arrangements. However, due to the complex spatial distri...

AI Technical Summary

Problems solved by technology

However, the ADMS system cannot establish a long-term chemical diffusion assessment, so it cannot perform CMAQ long-term series prediction correction

In addition to the CMAQ model, Geographic Information System (GIS) and Nested Air Quality Predictive Modeling System (NAQPMS) are also commonly used models to predict air pollutants, but they cannot handle a wide range of input variables due to relatively limited model capacity

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