Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

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 computational efficiency with a limited number of samples from the prediction distributions. Moreover, a novel algorithm called spatially-adjusted multivariate imputation by chained equation is also developed to take into account spatial correlations between nearby air-quality stations for correctly imputing the incoming data, and hence, to enable forecasting at a local scale. The LSTM-BNN framework is evaluated with observed datasets collected from stations and modeling outputs generated by the Conformal Cubic Atmospheric Model - Chemical Transport Model (CCAM-CTM) currently used in NSW. The airborne pollutants under investigation are $PM_{2.5}$ and ozone, which frequently exceed the standards. The results obtained from data fusion with our framework demonstrated high performance of the proposed LSTM-BNN model in air-pollutant prediction with reductions of over 30% in root mean square error compared to CCAM-CTM and over 50 % in inferring time compared to a DL model with Gaussian-based inference. Accuracy and reliability of the proposed model were also achieved with air pollution forecast in various seasons and suburbs.

Keywords

• Air pollution
• deep learning
• long short term memory
• spatial-correlated imputation
• time-series forecast
• uncertainty