A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization
agricultural market; carbon dioxide; carbon emission; Gross Domestic Product; optimization; support vector machine; Iran; carbon dioxide; algorithm; gross national product; Iran; support vector machine; Algorithms; Carbon Dioxide; Gross Domestic Product; Iran; Support Vector Machine
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-25885 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-258852023-05-29T17:05:25Z A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization Ehteram M. Sammen S.S. Panahi F. Sidek L.M. 57113510800 57192093108 55368172500 35070506500 agricultural market; carbon dioxide; carbon emission; Gross Domestic Product; optimization; support vector machine; Iran; carbon dioxide; algorithm; gross national product; Iran; support vector machine; Algorithms; Carbon Dioxide; Gross Domestic Product; Iran; Support Vector Machine The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:05:25Z 2023-05-29T09:05:25Z 2021 Article 10.1007/s11356-021-15223-4 2-s2.0-85111525151 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111525151&doi=10.1007%2fs11356-021-15223-4&partnerID=40&md5=e152e92eea718e13285060dce8208abc https://irepository.uniten.edu.my/handle/123456789/25885 28 46 66171 66192 Springer Science and Business Media Deutschland GmbH Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
agricultural market; carbon dioxide; carbon emission; Gross Domestic Product; optimization; support vector machine; Iran; carbon dioxide; algorithm; gross national product; Iran; support vector machine; Algorithms; Carbon Dioxide; Gross Domestic Product; Iran; Support Vector Machine |
| author2 |
57113510800 |
| author_facet |
57113510800 Ehteram M. Sammen S.S. Panahi F. Sidek L.M. |
| format |
Article |
| author |
Ehteram M. Sammen S.S. Panahi F. Sidek L.M. |
| spellingShingle |
Ehteram M. Sammen S.S. Panahi F. Sidek L.M. A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| author_sort |
Ehteram M. |
| title |
A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| title_short |
A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| title_full |
A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| title_fullStr |
A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| title_full_unstemmed |
A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization |
| title_sort |
hybrid novel svm model for predicting co2 emissions using multiobjective seagull optimization |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806424455491092480 |
| score |
13.222552 |