DOI: 10.1016/j.scitotenv.2019.135934
论文题名: A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms
作者: Sharma E. ; Deo R.C. ; Prasad R. ; Parisi A.V.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 709 语种: 英语
英文关键词: Artificial intelligence
; ICEEMDAN
; Particulate matter (PM2.5, PM10)
; Real-time air quality forecasts
; Visibility
Scopus关键词: Air quality
; Artificial intelligence
; Data handling
; E-learning
; Forecasting
; Forestry
; Health risks
; Knowledge acquisition
; Learning algorithms
; Linear regression
; Machine learning
; Mean square error
; Particles (particulate matter)
; Public health
; Risk assessment
; Signal processing
; Visibility
; Air quality forecasts
; Empirical Mode Decomposition
; Ensemble empirical mode decomposition
; ICEEMDAN
; Multiple linear regressions
; Online sequential extreme learning machine
; Partial autocorrelation function
; Particulate Matter
; Quality control
; air quality
; algorithm
; artificial intelligence
; decomposition analysis
; early warning system
; empirical analysis
; forecasting method
; machine learning
; modeling
; particulate matter
; visibility
; air quality
; algorithm
; article
; artificial intelligence
; autocorrelation
; empirical mode decomposition
; forecasting
; health care cost
; intrinsic mode function
; machine learning
; mortality
; noise
; particulate matter
; public health
; visibility
英文摘要: 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. © 2019 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158657
Appears in Collections: 气候变化与战略
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作者单位: Advanced Data Analytics: Environmental Modelling and Simulation Group, School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Science, School of Science and Technology, The University of Fiji, Fiji
Recommended Citation:
Sharma E.,Deo R.C.,Prasad R.,et al. A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms[J]. Science of the Total Environment,2020-01-01,709