KEYWORDS: Data modeling, Data fusion, Neural networks, Meteorology, Climatology, Temperature metrology, Education and training, Air temperature, Atmospheric modeling, Wind speed
Aiming at the problem of the single data source in PM2.5 prediction, a PM2.5 DNN-LSTM hybrid neural network prediction model that takes into account climate factors is proposed. First, the DNNnetwork is used to abstract the characteristics of climate and seasonal factors and climate factors as an additional part of the prediction process. Input and analyze in collaboration with LSTM network. Experiments with pollution data and weather data (sampling interval of one hour) are collected from monitoring sites in Beijing from 2010 to 2014, and comparing the DNN-LSTM model with other prediction models, the results show that this model is compared to LSTM. The RMSE of the model is reduced by 10.71%, which is 5.52% lower than the RMSE of the multi-source data fusion LSTM model. Research shows that the multi-source data fusion DNN-LSTM model proposed in this paper has better predictive ability. Compared with the LSTM model, the RMSE of this model is reduced by 10.71%, compared with the multi-source data fusion LSTM model, the RMSE is reduced by 5.52%, compared with the LSTM model, the MAE is reduced by 21.55%, and compared with the multi-source data fusion LSTM model, the RMSE is reduced by 12.94%.
Based on Landsat8 OLI/TIRS data, this paper studies the interaction relationship between the heat island and influencing factors of the core area of the capital. Combined with comprehensive analysis of multi-source data and spatial data exploration, the spatial autocorrelation pattern and spatial correlation between the heat island and influencing factors in the core area of the capital are analyzed, agglomeration mode. The spatial heterogeneity of influencing factors and the interaction between factors were analyzed by using multi-scale geographic weighted regression model and geographic detector model, and the main influencing factors of heat island were detected. The study found that the spatial and temporal distribution of thermal environment in the core area of the capital has obvious spatial autocorrelation; the multi-scale geographically weighted regression model has high fitting accuracy and rich model interpretation information, and the model relaxes the broadband information of different factors. Geographic detector factor detection found that building density, night light and POI were the main influencing factors of the heat island in the core area of the capital, and the factor interaction analysis found that the single factor effect in the core area of the capital was more significant, and there was a weak interaction between the factors.
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