一个增强的火焰优化极端学习机器混合模型用于预测CO2排放
Ahmed Ramdan Almaqtouf Algwil1, Wagdi M S Khalifa2
1Cyprus Health and Social Sciences University, Mersin 10, Turkey. 220928004@kstu.edu.tr.
Scientific reports
|April 8, 2025
概括
一个新的混合模型,基于高斯突变和收缩机制的火焰优化与极端学习机器 (GMSMFO-ELM),准确地预测二氧化碳排放. 这种先进的方法支持全球可持续性目标,具有高预测准确度.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 准确预测二氧化碳 (CO2) 排放对于有效的环境政策和可持续发展至关重要.
- 现有的预测模型往往在准确性和适应复杂的排放模式方面扎.
- 开发强大的混合模型可以显著提高对二氧化碳排放的预测能力.
研究的目的:
- 引入和评估一种新的混合模型,GMSMFO-ELM,用于精确的二氧化碳排放预测.
- 为了证明高斯突变和收缩机制基础的火焰优化 (GMSMFO) 算法在优化机器学习参数方面的有效性.
- 提供可靠的框架,为全球可持续发展倡议的知情决策提供可靠的框架.
主要方法:
- 将GMSMFO算法与极端学习机器 (ELM) 集成为混合预测模型.
- 利用高斯基基突变 (GM) 来增强人口多样性,以及缩小机制 (SM) 来改善GMSMFO内的勘探开发平衡.
- 在CEC2020基准套件上验证GMSMFO,并将其应用于微调ELM以预测二氧化碳排放.
主要成果:
- 在基准数据集上,GMSMFO算法表现出优于其他优化技术的性能.
- 在GMSMFO-ELM模型中,在二氧化碳排放预测方面,实现了96.5%的高确定系数 (R2).
- 该模型在RMSE,NRMSE,MAE和MSE等关键指标中表现优于现有的混合方法.
- 经济增长,外国直接投资和可再生能源被确定为二氧化碳排放的重要预测因素.
结论:
- 该GMSMFO-ELM模型展示了强大的和适应性性能,用于准确的二氧化碳排放预测.
- 这种混合方法为推进全球可持续发展目标提供了可靠的工具.
- 该研究强调了先进的优化算法的潜力,以提高环境建模和政策支持.
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