A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

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 hy...

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Vydáno v:The Science of the total environment Ročník 709; s. 135934
Hlavní autoři: Sharma, Ekta, Deo, Ravinesh C., Prasad, Ramendra, Parisi, Alfio V.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 20.03.2020
Témata:
air quality
algorithms
Artificial intelligence
atmospheric visibility
autocorrelation
health care costs
ICEEMDAN
monitoring
mortality
Particulate matter (PM2.5, PM10)
particulates
public health
Real-time air quality forecasts
risk reduction
trees
Visibility
r2
VIS
D
FDMS
μg/m3
ELM
Qld
ENS
NEPH
NEPM
SD
D-TEOM
WI
MLR
CHAG
PM2.5
CEEMDAN
EEMD
BMA
ICEEMDAN
EMD
WHO
DERM
PM2.5, iFOR
AI
OS-ELM
TEOM
RMSE
Real-time air quality forecasts
ppm
BAM
AQ
MAE
μm
PACF
r
PM2.5, iOBS
OEH
IMFs
NSW
PM10
Particulate matter (PM2.5, PM10)
Visibility
DoE
Artificial intelligence
Particulate matter (PM(2.5), PM)
ISSN:0048-9697, 1879-1026, 1879-1026
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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. [Display omitted] •An artificial intelligence predictive framework devised for air quality prediction.•Efficient forecasting of air quality with 5 modelling approaches was recorded.•OS-ELM coupled with ICEEMDAN outperformed the other models.•AI models show potential in health informatics and Australia's environment sector.•AI models can empower public health risk mitigation to a create liveable society.
AbstractList 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. [Display omitted] •An artificial intelligence predictive framework devised for air quality prediction.•Efficient forecasting of air quality with 5 modelling approaches was recorded.•OS-ELM coupled with ICEEMDAN outperformed the other models.•AI models show potential in health informatics and Australia's environment sector.•AI models can empower public health risk mitigation to a create liveable society.
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.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.
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.
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, PM ; Particulate Matter 10, PM 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 PM , PM , 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 (E ) and Nash-Sutcliffe coefficients (E ) 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 PM E 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., PM & 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/m (MAE), 1.01-1.47 μg/m (RMSE) for PM whereas for PM , these metrics registered a value of 1.29-3.84 μg/m (MAE), 3.01-7.04 μg/m (RMSE) and for Visibility, they were 0.01-3.72 μg/m (MAE (Mm )), 0.04-5.98 μg/m (RMSE (Mm )). 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.
ArticleNumber 135934
Author Deo, Ravinesh C.
Parisi, Alfio V.
Sharma, Ekta
Prasad, Ramendra
Author_xml – sequence: 1
  givenname: Ekta
  surname: Sharma
  fullname: Sharma, Ekta
  email: ekta.sharma@usq.edu.au
  organization: Advanced Data Analytics: Environmental Modelling and Simulation Group, School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
– sequence: 2
  givenname: Ravinesh C.
  orcidid: 0000-0002-2290-6749
  surname: Deo
  fullname: Deo, Ravinesh C.
  email: ravinesh.deo@usq.edu.au
  organization: Advanced Data Analytics: Environmental Modelling and Simulation Group, School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
– sequence: 3
  givenname: Ramendra
  surname: Prasad
  fullname: Prasad, Ramendra
  organization: Department of Science, School of Science and Technology, The University of Fiji, Fiji
– sequence: 4
  givenname: Alfio V.
  surname: Parisi
  fullname: Parisi, Alfio V.
  email: alfio.parisi@usq.edu.au
  organization: Advanced Data Analytics: Environmental Modelling and Simulation Group, School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31869708$$D View this record in MEDLINE/PubMed
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VIS
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FDMS
μg/m3
ELM
Qld
ENS
NEPH
NEPM
SD
D-TEOM
WI
MLR
CHAG
PM2.5
CEEMDAN
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WHO
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AI
OS-ELM
TEOM
RMSE
Real-time air quality forecasts
ppm
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AQ
MAE
μm
PACF
r
PM2.5, iOBS
OEH
IMFs
NSW
PM10
Particulate matter (PM2.5, PM10)
Visibility
DoE
Artificial intelligence
Particulate matter (PM(2.5), PM)
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Snippet Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges...
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StartPage 135934
SubjectTerms air quality
algorithms
Artificial intelligence
atmospheric visibility
autocorrelation
health care costs
ICEEMDAN
monitoring
mortality
Particulate matter (PM2.5, PM10)
particulates
public health
Real-time air quality forecasts
risk reduction
trees
Visibility
Title A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms
URI https://dx.doi.org/10.1016/j.scitotenv.2019.135934
https://www.ncbi.nlm.nih.gov/pubmed/31869708
https://www.proquest.com/docview/2330337165
https://www.proquest.com/docview/2388784624
Volume 709
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