![](/images/graphics-bg.png)
A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration
Joint Authors
Ali Shah, Syed Ahsin
Ahmed Nadeem, Malik Sajjad
Almaraashi, Majid
Shim, Seong-O.
Habeebullah, Turki M.
Aziz, Wajid
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The prediction of atmospheric particulate matter (APM) concentration is essential to reduce adverse effects on human health and to enforce emission restrictions.
The dynamics of APM are inherently nonlinear and chaotic.
Phase space reconstruction (PSR) is one of the widely used methods for chaotic time series analysis.
The APM mass concentrations are an outcome of complex anthropogenic contributors evolving with time, which may operate on multiple time scales.
Thus, the traditional single-variable PSR-based prediction algorithm in which data points of last embedding dimension are used as a target set may fail to account for multiple time scales inherent in APM concentrations.
To address this issue, we propose a novel PSR-based scientific solution that accounts for the information contained at multiple time scales.
Different machine learning algorithms are used to evaluate the performance of the proposed and traditional PSR techniques for predicting mass concentrations of particulate matter up to 2.5 micron (PM2.5), up to 10 micron (PM10.0), and ratio of PM2.5/PM10.0.
Hourly time series data of PM2.5 and PM10.0 mass concentrations are collected from January 2014 to September 2015 at the Masfalah air quality monitoring station (couple of kilometers from the Holy Mosque in Makkah, Saudi Arabia).
The performances of various learning algorithms are evaluated using RMSE and MAE.
The results demonstrated that prediction error of all the machine learning techniques is smaller for the proposed PSR approach compared to traditional approach.
For PM2.5, FFNN leads to best results (both RMSE and MAE 0.04 μgm−3), followed by SVR-L (RMSE 0.01 μgm−3 and MAE 0.09 μgm−3) and RF (RMSE 1.27 μgm−3 and MAE 0.86 μgm−3).
For PM10.0, SVR-L leads to best results (both RMSE and MAE 0.06 μgm−3), followed by FFNN (RMSE 0.13 μgm−3 and MAE 0.09 μgm−3) and RF (RMSE 1.60 μgm−3 and MAE 1.16 μgm−3).
For PM2.5/PM10.0, FFNN is the best and accurate method for prediction (0.001 for both RMSE and MAE), followed by RF (0.02 for both RMSE and MAE) and SVR-L (RMSE 0.05 μgm−3 and MAE 0.04).
American Psychological Association (APA)
Ali Shah, Syed Ahsin& Aziz, Wajid& Ahmed Nadeem, Malik Sajjad& Almaraashi, Majid& Shim, Seong-O.& Habeebullah, Turki M.. 2019. A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration. Scientific Programming،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210748
Modern Language Association (MLA)
Ali Shah, Syed Ahsin…[et al.]. A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration. Scientific Programming No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1210748
American Medical Association (AMA)
Ali Shah, Syed Ahsin& Aziz, Wajid& Ahmed Nadeem, Malik Sajjad& Almaraashi, Majid& Shim, Seong-O.& Habeebullah, Turki M.. A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210748
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210748