| Autor: |
Huynh A. D. Nguyen, Quang P. Ha, Hiep Duc, Merched Azzi, Ningbo Jiang, Xavier Barthelemy, Matthew Riley |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 35710-35725 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3265725 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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