Innovative SVM optimization with differential gravitational fireworks for superior air pollution classification

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Growing conflict between environmental protection and economic growth necessitates improved air quality management.
• Accurate assessment and remediation of air pollution are critical for public health and ecological balance.

Purpose of the Study:

• To develop and validate an advanced computational approach for air pollution assessment and remediation.
• To enhance the performance of Support Vector Machine (SVM) models for air quality data analysis.

Main Methods:

• Utilized an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm for SVM optimization.
• Implemented sophisticated data preprocessing and augmentation techniques, including the differential evolution algorithm.
• Employed sequential and nonsequential data fusion strategies for comprehensive analysis.

Main Results:

• The optimized SVM model demonstrated superior classification accuracy compared to conventional methods.
• Achieved a peak accuracy of 91% using the sequential data fusion method, outperforming nonsequential techniques by at least 3%.
• Successfully mitigated overfitting issues through advanced parameter optimization.

Conclusions:

• The proposed integrated approach offers a highly effective technological solution for precise air pollution measurement and control.
• This study highlights the potential of advanced optimization algorithms in improving environmental monitoring and management systems.
• The findings provide a robust framework for addressing air quality challenges in the context of sustainable development.