Sensitivity Analysis results of key parameters.

To address the challenges of increasing carbon dioxide (CO2) emissions and climate change caused by the growth of air traffic, accurate prediction of CO2 emissions in civil aviation has become crucial. This study proposes a CO2 emission prediction method based on an improved back propagation (BP) neural network, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the hyperparameters of the BP neural network, thereby enhancing the prediction capability for CO2 emissions in civil aviation. To overcome the limitations of the traditional SSA, such as the tendency to fall into local optima during population initialization and the search process, this paper introduces Tent mapping for population initialization and incorporates adaptive t-distribution-based perturbation for individual position updates during the mutation operation, aiming to improve the algorithm’s global search ability and convergence performance. Subsequently, the ISSA algorithm is applied to optimize the weights and biases of the BP neural network, further constructing an ISSA-BP neural network-based prediction model for civil aviation CO2 emissions. Experimental results demonstrate that the improved BP neural network outperforms other comparative models in terms of prediction accuracy and error control, enabling accurate prediction of civil aviation CO2 emissions. This research provides a solid theoretical foundation for formulating precise energy-saving and emission-reduction strategies in civil aviation.

为应对航空运输增长引发的二氧化碳（CO2）排放加剧与气候变化挑战，精准预测民用航空二氧化碳排放已成为至关重要的课题。本研究提出一种基于改进反向传播（BP）神经网络的二氧化碳排放预测方法，采用改进麻雀搜索算法（ISSA）优化BP神经网络的超参数，以此提升民用航空二氧化碳排放的预测能力。针对传统麻雀搜索算法在种群初始化与搜索过程中易陷入局部最优的局限，本文引入Tent映射完成种群初始化，并在变异操作中加入基于自适应t分布的扰动以更新个体位置，旨在提升算法的全局搜索能力与收敛性能。随后，利用ISSA算法优化BP神经网络的权重与偏置，进一步构建基于ISSA-BP神经网络的民用航空二氧化碳排放预测模型。实验结果表明，该改进BP神经网络在预测精度与误差控制方面均优于其他对比模型，可实现民用航空二氧化碳排放的精准预测。本研究可为民用航空领域制定精准的节能减排策略提供坚实的理论基础。