Loading...
CO2 Emission Predication Based on an Improved Swarm Optimization Technique
Hamed. M. Aly, Rabab
Hamed. M. Aly, Rabab
Type
Supervisor
Date
2026-01-26
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Forecasting CO2 emissions is one of the most important measures governments must take to maintain human health in the current climate. CO2 levels are a critical component of overall air quality and are closely linked to industrial activity. Industrial air pollution, particularly CO2 emissions, remains one of the most significant challenges faced today. The use of optimization algorithms can support accurate predictions of CO2 emissions. This paper proposes an optimization strategy based on the proposed Bee- Particle Swarm optimization (BPSO) method to estimate CO2 emissions. The proposed method integrates Bee Optimization and enhances it using particle swarm technique to achieve higher predictive accuracy for CO2 emission forecasting. The model presented in this paper consists of two stages: the first involves data preprocessing using the Discrete Wavelet Transform (DWT), and the second involves feature selection and CO2 emission prediction using the proposed BPSO algorithm. The results show that an increased number of features correlates with a heightened potential for improved accuracy. The outcomes demonstrate the effectiveness of the proposed methodology.
Department
Publisher
Sponsor
No