A hybrid air quality early-warning framework: an hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms
Article
Article Title | A hybrid air quality early-warning framework: an hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms |
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ERA Journal ID | 3551 |
Article Category | Article |
Authors | Sharma, Ekta (Author), Deo, Ravinesh C. (Author), Prasad, Ramendra (Author) and Parisi, Alfio V. (Author) |
Journal Title | Science of the Total Environment |
Journal Citation | 709, pp. 1-23 |
Article Number | 135934 |
Number of Pages | 23 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0048-9697 |
1879-1026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scitotenv.2019.135934 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0048969719359297 |
Abstract | Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM2.5; Particulate Matter 10, PM10 and lower atmospheric visibility, VIS), the atmospheric variables associated with increased respiratory induced mortality and recurrent health-care cost. Firstly, hourly air quality data series (January-2015 to December-2017) are demarcated into their respective intrinsic mode functions (IMFs) and a residual sub-series that reveal patterns and resolve data complexity characteristics, followed by partial autocorrelation function applied to each IMF and residual sub-series to unveil historical changes in air quality. To design the prescribed hybrid model, the data is partitioned into training (70%), validation (15%) and testing (15%) sub-sets. The online sequential-extreme learning machine (OS-ELM) algorithm integrated with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is designed as a data pre-processing system to robustly extract predictive patterns and fine-tune the model generalization to a near-optimal global solution, which represents modelled air quality at hourly forecast horizons. The resulting early warning AI-based framework denoted as ICEEMDAN-OS-ELM model, is individually constructed by forecasting each IMF and residual sub-series, with hourly PM2.5, PM10, and VIS obtained by the aggregated sum of forecasted IMFs and residual sub-series. The results are benchmarked with many competing predictive approaches; e.g., hybrid ICEEMDAN-multiple-linear regression (MLR), ICEEMDAN-M5 model tree and standalone versions: OS-ELM, MLR, M5 model tree. Statistical metrics including the root-mean-square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Legates & McCabe's Index (ELM) and Nash–Sutcliffe coefficients (ENS) are used to evaluate the model's accuracy. Both visual and statistical results show that the proposed ICEEMDAN-OS-ELM model registers superior results, outperforming alternative comparison approaches. For instance, for PM2.5, ELM values ranged from 0.65–0.82 vs. 0.59–0.77 for ICEEMDAN-M5 tree, 0.59–0.74 for ICEEMDAN-MLR, 0.28–0.54 for OS-ELM, 0.27–0.54 for M5 tree and 0.25–0.53 for the MLR model. For remaining air quality variables (i.e., PM10 & VIS), the objective model (ICEEMDAN-OS-ELM) outperformed the comparative models. In particular, ICEEMDAN-OS-ELM registered relatively low RMSE/MAE, ranging from approximately 0.7–1.03 μg/m3 (MAE), 1.01–1.47 μg/m3 (RMSE) for PM2.5 whereas for PM10, these metrics registered a value of 1.29–3.84 μg/m3 (MAE), 3.01–7.04 μg/m3 (RMSE) and for Visibility, they were 0.01–3.72 μg/m3 (MAE (Mm−1)), 0.04–5.98 μg/m3 (RMSE (Mm−1)). Visual analysis of forecasted and observed air quality through a Taylor diagram illustrates the objective model's preciseness, confirming the versatility of early warning AI-model in generating air quality forecasts. The excellent performance ascertains the hybrid model's potential utility for air quality monitoring and subsequent public health risk mitigation. |
Keywords | real-time air quality forecasts; particulate matter (PM2.5, PM10); visibility; artificial intelligence; ICEEMDAN |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
429999. Other health sciences not elsewhere classified | |
410499. Environmental management not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
University of Fiji, Fiji | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5812/a-hybrid-air-quality-early-warning-framework-an-hourly-forecasting-model-with-online-sequential-extreme-learning-machines-and-empirical-mode-decomposition-algorithms
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