Abstract:This paper presents a data fusion framework to enhance the accuracy of air-pollutant forecast in the state of New South Wales (NSW), Australia using deep learning (DL) as a core model. Here, we propose a long short-term memory Bayesian neural network (LSTM-BNN) to improve performance of the predictive profiles via quantifying uncertainties and adjusting model parameters. For this, we develop a new inferring technique for kernel density estimation with subdivision tuning to ensure both forecast accuracy and com… Show more
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