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Using the Hybrid MLR-GA Approach for Air Pollution Forecasting | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 3, Volume 16, Issue 2, 2019, Pages 25-36 PDF (1.04 M) | ||
| DOI: 10.33899/iqjoss.2019.0164190 | ||
| Authors | ||
| Fanar Abdul Razaq Mohammed* 1; Osama Basheer Hannon2 | ||
| 1Researcher / Directorate General of Education in Nineveh Governorate | ||
| 2Lecturer / Department of Statistics and Informatics / College of Computer Science and Mathematics / University of Mosul | ||
| Abstract | ||
| Air Quality Modeling gained great importance in atmospheric pollution because of its negative effects on the environment and human health. In our study, the relationship between (Particulate Matter PM10) and other nine variables over three years is studied to applied the multiple linear regression models. The seasonal influences for seasonally periods lead to difficult analyzing and forecasting. Therefore, Time-stratified (TS) approach is used into seasonally. The multiple linear regression (MLR) model is the most common for studying like this number of variables. Genetic algorithm (GA) as well as their hybrid method such as MLR–GA, is proposed to reduce the number of studied variables. Reducing the number of variables may also lead to more accurate results. The genetic algorithm has improved the performance of MLR method separately. GA also improved MLR performance by using hybrid method MLR-GA. | ||
| Keywords | ||
| multiple linear regression; genetic algorithm; Forecasting | ||
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