航空旅客碳抵消和碳中和战略:通过卷积神经网络的实施机制
Hongwei Zhou1,2
1School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China.
Heliyon
|October 9, 2024
概括
这项研究优化了一个卷积神经网络,用于预测航空碳排放,实现高精度. 改进后的模型为环境可持续性提供了更好的预测稳定性和战略适应性.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
背景情况:
- 航空业面临着重大的碳排放挑战.
- 目前的碳抵消和碳中和战略需要增强的预测建模.
- 现有的研究缺乏用于准确预测航空碳排放的先进人工智能.
研究的目的:
- 分析航空碳排放策略和研究结果.
- 优化卷积神经网络 (CNN) 以提高预测准确性和效率.
- 用实证数据与其他先进模型对拟议的优化CNN进行验证.
主要方法:
- 优化卷积神经网络 (CNN) 具有架构改进,注意力机制和多尺度特征提取.
- 对变压器网络,图形卷积网络,囊网络,生成对抗网络和时间卷积网络进行比较分析.
- 通过准确性,精度,回忆,F1分数,预测稳定性,战略适应性,响应时间和长期有效性模拟进行验证.
主要成果:
- 优化的CNN实现了卓越的精度 (0.942),精度 (0.967),回忆 (0.951) 和F1得分 (0.934).
- 在模拟中证明了高预测稳定性 (0.944) 和战略适应性 (0.953).
- 显示了快速响应时间 (0.04s在1000个数据量) 和计算效率随着数据的增加.
结论:
- 优化的CNN为航空碳排放预测和战略优化提供了非常有效的工具.
- 与现有方法相比,该模型显示了性能和效率的显著改善.
- 这项研究通过先进的人工智能应用,为航空部门的环境可持续性做出了贡献.
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