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A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization.

Mohammad Ehteram1, Saad Sh Sammen2, Fatemeh Panahi3

  • 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil 4 Engineering, Semnan University, Semnan, Iran.

Environmental Science and Pollution Research International
|July 31, 2021
PubMed
Summary

This study developed an inclusive multiple model (IMM) to accurately predict carbon dioxide (CO2) emissions from Iran's agricultural sector. The IMM significantly improved prediction accuracy compared to individual models, highlighting its effectiveness for environmental policy.

Keywords:
CO2 emissionsEnvironment managementMultiobjective algorithmsSVM

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Area of Science:

  • Environmental Science
  • Agricultural Economics
  • Computational Intelligence

Background:

  • The agricultural sector is a significant contributor to carbon dioxide (CO2) emissions globally.
  • Accurate prediction of CO2 emissions is crucial for developing effective environmental mitigation strategies.
  • Previous models may not fully capture the complex relationships influencing agricultural CO2 emissions.

Purpose of the Study:

  • To predict carbon dioxide (CO2) emissions in the agricultural sectors of 25 Iranian provinces.
  • To evaluate the performance of Support Vector Machine (SVM) models enhanced by multiobjective algorithms (MOAs).
  • To introduce and assess an inclusive multiple model (IMM) for improved CO2 emission prediction.

Main Methods:

  • Utilized Support Vector Machine (SVM) models for CO2 emission prediction.
  • Employed multiobjective algorithms, including seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO), to optimize SVM models.
  • Developed an inclusive multiple model (IMM) integrating outputs from the optimized SVM models.

Main Results:

  • The radial basis function was identified as the optimal kernel for the SVM-MOSOA model.
  • The optimal input combination included gross domestic product (GDP), squared GDP (GDP^2), energy use, and Gini index.
  • The IMM demonstrated superior performance, reducing mean absolute errors by 24-76% compared to individual SVM-MOA models during training.

Conclusions:

  • The inclusive multiple model (IMM) significantly enhances the accuracy of CO2 emission predictions from the agricultural sector.
  • The IMM approach offers a robust framework for environmental modeling and policy development.
  • The study underscores the importance of considering economic and social factors (GDP, income inequality) in predicting agricultural CO2 emissions.