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《Geomatics World》 2018-03
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The Prediction of Air Pollutants Based on Full Connection and LSTM Neural Network

HAN Wei;WU Yanlan;REN Fu;School of Resources and Environmental Science, Wuhan University;School of Resources and Environmental Science;  
Air pollution has been an increasing challenge for many countries nowadays. It is of great necessary to forecast the quantity and spatial distribution of air pollutants. This paper proposes a novel ensemble method for air quality forecasting based on neural network. Via the combination of full-connection neural network and LSTM, the different spatial-temporal features of air pollutants concentration data and weather data are obtained with ensemble method. Comparing with traditional single methods, the ensemble method which depends on the form of ensemble of full-connection neural network and LSTM can not only get over the limitations of single model, but also improve the accuracy of forecasting. Finally, taking Wuhan as an example, the experimental results show that the hybrid model is more accurate than the single model in predicting air pollutants.
【Fund】: 国家自然基金项目(41571438)资助
【CateGory Index】: TP183;X51
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