Air Pollution Forecasting using Convolution based Long Short Term Memory Techniques
Keywords:
CBLSTM, Air Pollution, Air QualityAbstract
During the past few years, severe air-pollution problem has garnered worldwide attention due to its effect on health and well-being of individuals. As a result, the analysis and prediction of air pollution has attracted a good deal of interest among researchers. The research areas include traditional machine learning, neural networks and deep learning. How to effectively and accurately predict air pollution becomes an important issue. In this thesis, we propose an convolution based Long Short Term Memory (CBLSTM) based on the LSTM deep learning method. In this new model, we combine local air quality monitoring station, the station in nearby industrial areas, and the stations for external pollution sources. To improve prediction accuracy, we aggregate CBLSTM models into a predictive model for early predictions based on external sources of pollution and information from nearby industrial air quality stations.
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