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航空运输二氧化碳排放预测:一个改进的反向传播神经网络
Peiwen Zhang1,2,3, Yunan Luo1, Qian Yu1
1School of Economics and Management, Civil Aviation Flight University of China, Guanghan, China.
PloS one
|October 7, 2025
概括
准确预测民用航空二氧化碳 (CO2) 排放量至关重要. 这项研究引入了改进的神经网络模型,提高了有效减排战略的预测准确性.
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:
- 增加的空中交通通过增加的二氧化碳 (CO2) 排放量为气候变化做出了贡献.
- 准确预测民用航空的二氧化碳排放对于制定减排战略至关重要.
- 现有的预测模型在准确性和效率方面面临挑战.
研究的目的:
- 为民用航空开发先进的二氧化碳排放预测模型.
- 提高民用航空二氧化碳排放预测的预测准确度和可靠性.
- 为支持航空领域的节能和减排倡议提供强有力的方法.
主要方法:
- 开发了一种改进的背向传播 (BP) 神经网络.
- 改进的搜索算法 (ISSA) 被用于优化BP网络的超参数.
- 帐映射和自适应的t分布扰动被纳入ISSA以增强全球搜索能力.
主要成果:
- 与其他模型相比,ISSA优化的BP神经网络显示出更高的预测准确性.
- 拟议的模型显示,在预测民用航空二氧化碳排放方面,改进了错误控制.
- 实验验证证了该模型在预测二氧化碳排放方面的有效性.
结论:
- ISSA-BP神经网络模型提供了一个非常准确的方法来预测民用航空的二氧化碳排放.
- 这项研究为实施航空部门有效的减排政策提供了强有力的理论基础.
- 该研究通过改进的预测建模,有助于解决航空旅行对环境的影响.
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