A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting
Abstract This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (D...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86275-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594697617408000 |
|---|---|
| author | Essam H. Houssein Meran Mohamed Eman M. G. Younis Waleed M. Mohamed |
| author_facet | Essam H. Houssein Meran Mohamed Eman M. G. Younis Waleed M. Mohamed |
| author_sort | Essam H. Houssein |
| collection | DOAJ |
| description | Abstract This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter ( $$PM_{2.5}$$ ) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results. |
| format | Article |
| id | doaj-art-721bb83b7806447e8f9e6395be562b14 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-721bb83b7806447e8f9e6395be562b142025-01-19T12:24:10ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-86275-6A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecastingEssam H. Houssein0Meran Mohamed1Eman M. G. Younis2Waleed M. Mohamed3Faculty of Computers and Information, Minia UniversityFaculty of Computers and Information, Minia UniversityFaculty of Computers and Information, Minia UniversityFaculty of Computers and Information, Minia UniversityAbstract This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter ( $$PM_{2.5}$$ ) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.https://doi.org/10.1038/s41598-025-86275-6Support Vector Regression (SVR)Particulate MatterAir QualityMetaheuristicsHarris Hawks Optimization (HHO) |
| spellingShingle | Essam H. Houssein Meran Mohamed Eman M. G. Younis Waleed M. Mohamed A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting Scientific Reports Support Vector Regression (SVR) Particulate Matter Air Quality Metaheuristics Harris Hawks Optimization (HHO) |
| title | A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting |
| title_full | A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting |
| title_fullStr | A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting |
| title_full_unstemmed | A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting |
| title_short | A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting |
| title_sort | hybrid harris hawks optimization with support vector regression for air quality forecasting |
| topic | Support Vector Regression (SVR) Particulate Matter Air Quality Metaheuristics Harris Hawks Optimization (HHO) |
| url | https://doi.org/10.1038/s41598-025-86275-6 |
| work_keys_str_mv | AT essamhhoussein ahybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT meranmohamed ahybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT emanmgyounis ahybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT waleedmmohamed ahybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT essamhhoussein hybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT meranmohamed hybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT emanmgyounis hybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting AT waleedmmohamed hybridharrishawksoptimizationwithsupportvectorregressionforairqualityforecasting |