通过混合模型改进二氧化碳排放预测,利用先进的子搜索算法
Si-Yuan Ma1, Xiao-Kang Wang2, Sijia Cheng3
1School of Business, Central South University, Changsha, People's Republic of China.
Environmental technology
|February 16, 2025
概括
由于数据有限,准确预测二氧化碳 (CO2) 排放具有挑战性. 本研究介绍了一种使用特征选择和优化最小方程支持向量机的新型混合模型,以改进二氧化碳排放预测.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 增加的二氧化碳 (CO2) 排放导致全球变暖和气候变化,威胁人类发展和生态系统.
- 现有的二氧化碳排放预测模型与稀缺数据,固有的不确定性和数据波动性作斗争.
- 准确的预测对于有效的气候变化减缓战略至关重要.
研究的目的:
- 开发一种新的混合模型,以准确预测二氧化碳排放,特别是在有限的数据.
- 通过有效的特征选择和模型参数优化来提高预测性能.
- 用中国的碳排放数据验证模型的有效性.
主要方法:
- 为了可靠的特征选择,开发了一种稳定的特征选方法.
- 使用最小平方支向量机 (LSSVM) 来预测有限样本的周期序列.
- 使用改进的Sparrow Search Algorithm (SSA) 优化了LSSVM参数,其中包括Sin混乱映射,自适应惯性权重和Cauchy-Gauss变量.
主要成果:
- 与标准方法相比,改进的SSA表现出卓越的全球优化能力和更快的融合速度.
- 拟议的混合模型实现了与实际数据的最高一致性,大大提高了预测准确性.
- 对比实验验证实了该模型对现有方法的优越性.
结论:
- 新的混合模型有效地解决了利用有限和复杂的数据对二氧化碳排放预测的挑战.
- 优化的特征选择和LSSVM与增强的SSA提供了一个强大的框架,用于准确的环境预测.
- 这种方法在预测气候变化研究和政策制定中的二氧化碳排放方面取得了重大进展.
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