Secondary Modeling of Air Quality Based on LSTM Cycle Neural Network

Abstract

Based on the “primary prediction data” generated by the operation of the commonly used air quality prediction model WRF-CMAQ and the “measured data” obtained from the actual monitoring of the air quality monitoring stations, this paper establishes the air quality prediction constant value by comprehensively applying the long short-term memory (LSTM) neural network algorithm and other methods. Secondly, the meteorological conditions are reasonably classified, and the K-Means algorithm is used to eliminate the influence of the dimensional difference between the data. The original data is missing or abnormal. In this paper, the mixed interpolation method is proposed to deal with the missing data, and the isolated forest algorithm is used to deal with the abnormal data. Thirdly, three possible classification schemes are found by using the method of Elbow Method, and the correlation analysis and data visualization methods are used respectively. After discussing the three meteorological classification conditions, the optimal classification number is determined to be 4 categories. Finally, a model is established which is applicable to A, B and C, three monitoring points, and verify the rationality of the modeling and forecasting process and the robustness of the model.