Comparisons between logistic regression and support vector machine for air pollution datasets forecasting
Joint Authors
Muhammad, Surah Amir
Hannun, Usamah Bashir
Source
Iraqi Journal of Statistical Science
Issue
Vol. 17, Issue 31 (30 Jun. 2020), pp.49-64, 16 p.
Publisher
University of Mosul College of Computer Science and Mathematics
Publication Date
2020-06-30
Country of Publication
Iraq
No. of Pages
16
Main Subjects
Economics & Business Administration
Abstract EN
Particular matter (PM10) studying and forecasting is necessary to control and reduce the damage of environment and human health.
There are many pollutants as sources of air pollution (Co, So2, O 3, Nox, No, Wind Speed, and Ambient Temperature) may effect on PM10 variable.
PM10 and the pollutant variables have been taken from the meteorological station in Kuala Lumpur, Malaysia.
All of these variables classified as nonlinear data.
Logistic regression (LR) model can be used for modeling and forecasting these multivariable datasets.
LR is one of linear statistical methods, therefore it may reflect inaccurate results when used with nonlinear datasets.
To improve the results of forecasting, support vector machine (SVM) method has been suggested in this study.
The results in this study reflect outperforming for SVM method comparing to LR.
In conclusion, SVM forecasting can be used for more accuracy with nonlinear multivariate datasets when PM10 is as dependent variable.
American Psychological Association (APA)
Muhammad, Surah Amir& Hannun, Usamah Bashir. 2020. Comparisons between logistic regression and support vector machine for air pollution datasets forecasting. Iraqi Journal of Statistical Science،Vol. 17, no. 31, pp.49-64.
https://search.emarefa.net/detail/BIM-1334451
Modern Language Association (MLA)
Muhammad, Surah Amir& Hannun, Usamah Bashir. Comparisons between logistic regression and support vector machine for air pollution datasets forecasting. Iraqi Journal of Statistical Science Vol. 17, no. 31 (2020), pp.49-64.
https://search.emarefa.net/detail/BIM-1334451
American Medical Association (AMA)
Muhammad, Surah Amir& Hannun, Usamah Bashir. Comparisons between logistic regression and support vector machine for air pollution datasets forecasting. Iraqi Journal of Statistical Science. 2020. Vol. 17, no. 31, pp.49-64.
https://search.emarefa.net/detail/BIM-1334451
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 62-64
Record ID
BIM-1334451