This paper proposes an Improved Lemur Optimization algorithm (ILO), which combines the advantages of the Spider Monkey Optimization algorithm, Simulated Annealing algorithm, and Lemur Optimization algorithm. Through the use of an adaptive nonlinear decrement model, adaptive learning factors, and updated jump rates, the algorithm enhances its global exploration and local exploitation capabilities. A Gaussian function model is used to simulate the mountain environment, and a mathematical model for UAV flight is established based on constraints and objective functions. The fitness function is employed to determine the minimum cost for avoiding obstacles in a designated airspace, and cubic spline interpolation is used to smooth the flight path. The Improved Lemur Optimization algorithm was tested using the CEC2017 benchmark set, assessing its search capability, convergence speed, and accuracy. The simulation results show that ILO generates high-quality, smooth paths with fewer iterations, overcoming the issues of premature convergence and insufficient local search ability in traditional genetic algorithms. It adapts to complex terrain, providing an efficient and reliable solution.

Confronted with the imperatives of sustainable development within the civil aviation sector, the precision in prognostication of aircraft fuel expenditure emerges as a critical imperative. To overcome the shortcomings in the current study of aircraft range fuel prediction, we propose a novel fuel consumption prediction model that integrates Wavelet Packet Decomposition (WPD) with an Improved Arctic Puffin Optimization (IAPO) optimized Bidirectional Long-Short-Term Memory network–Kolmogorov-Arnold network (BiLSTM–KAN). Initially, Pearson’s correlation coefficient is employed to select the most significant features. Subsequently, WPD decomposes the raw fuel consumption data into subsequences across various frequency bands. The BiLSTM network effectively captures long-term dependency features within the sequence data, which are then input into KAN to further elucidate the complex nonlinear relationships present in the data. Additionally, the SPM chaotic mapping strategy is utilized for population initialization, while the introduction of the golden sine operator variation strategy enhances the local search capabilities of the algorithm. The adaptive swoop switching strategy adjusts the search intensity, thereby improving the global search performance and convergence speed of the Arctic Puffin Optimization (APO). Ultimately, the multi-strategy improved APO is employed to optimize the hyperparameters of the BiLSTM–KAN model, allowing for the superposition of each subsequence to yield the final prediction results. Experimental results indicate that in the B737 aircraft model, the Mean Squared Error, Normalized Root Mean Squared Error (NRMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²) of the proposed WPD–IAPO–BiLSTM–KAN model are 34.57, 0.0061, 3.81, and 0.9974, respectively. In the A320 aircraft model, the MSE, NRMSE, MAPE, and R² of this model are 40.71, 0.0078, 2.61, and 0.9934, respectively. In the B747 aircraft model, the MSE, NRMSE, MAPE, and R² of the model are 242.17, 0.0110, 3.88, and 0.9828, respectively. The WPD–IAPO–BiLSTM–KAN model surpasses other comparative models in prediction accuracy, exhibiting a low prediction error. This model represents a novel and effective approach to predicting airline fuel consumption and provides valuable insights for reducing aircraft fuel consumption.

We utilized the International Civil Aviation Organization (ICAO) standard emission model, refined with adjustments for fuel flow, LTO cycle work mode time, and emission indices, to investigate the environmental footprint of airports at different altitudes. Airports categorized as high (above 5000 $ \mathrm{f}\mathrm{t} $), medium (1500–5000 $ \mathrm{f}\mathrm{t} $), and low (below 1500 $ \mathrm{f}\mathrm{t} $) altitudes were selected to provide a comprehensive representation of the altitude spectrum. The analysis was anchored over the period spanning 2016 to 2017. Emission inventories for air pollutants and noise were computed for these airports, focusing on the LTO (Landing and Take-Off) cycle. Our findings indicated that high altitude airports exhibit the highest $ {\mathrm{N}\mathrm{O}}_{\mathrm{x}} $ emissions, reaching 406.4 $ \mathrm{t} $, whereas low altitude airports record the highest noise levels at 73.1 $ \mathrm{d}\mathrm{B} $. Significant disparities in emission profiles were observed across different phases of the LTO cycle at airports of varying altitudes. Notably, during the climb phase, the types and proportion of $ {\mathrm{N}\mathrm{O}}_{\mathrm{x}} $ emissions at high altitude airports were as high as 71.8%, contrasting with the 45.6% at low altitude airports. Additionally, emissions of gaseous pollutants from major aircrafts, exemplified by the A320 model, escalated with altitude. Specifically, $ {\mathrm{N}\mathrm{O}}_{\mathrm{x}} $ emissions increased from 10.55 $ \mathrm{k}\mathrm{g}/\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e} $ at low altitude to 20.48 $ \mathrm{k}\mathrm{g}/\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e} $ at high altitude, and $ \mathrm{C}\mathrm{O} $ emissions from 10.88 $ \mathrm{k}\mathrm{g}/\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e} $ to 22.89 $ \mathrm{k}\mathrm{g}/\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e} $. A robust correlation between $ {\mathrm{N}\mathrm{O}}_{\mathrm{x}} $ emissions and $ {L}_{den} $ was identified among airports at different altitudes, with correlation coefficients of 0.96 for low altitude, 0.97 for medium altitude, and 0.93 for high altitude airports. This study delineates the distinct characteristics of air pollutant and noise emissions from airports across altitudes, offering novel insights for the environmental assessment of airport operations.

Introduction: To address the challenges of current 4D trajectory prediction—specifically, limited multi-factor feature extraction and excessive computational cost—this study develops a lightweight prediction framework tailored for real-time air-traffic management.Methods: We propose a hybrid RCBAM–TCN–LSTM architecture enhanced with a teacher–student knowledge distillation mechanism. The Residual Convolutional Block Attention Module (RCBAM) serves as the teacher network to extract high-dimensional spatial features via residual structures and channel–spatial attention. The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. Historical ADS-B trajectory data from Zhuhai Jinwan Airport are preprocessed using cubic spline interpolation and a uniform-step sliding window to ensure data alignment and temporal consistency. In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student trainingResults: In multi-step prediction experiments, the distilled RCBAM–TCN–LSTM model achieved average reductions of 40%–60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN–LSTM models, while improving R^² by 4%–6%. The approach maintained high accuracy across different prediction horizons while reducing computational complexity.Discussion: The proposed method effectively balances high-precision modeling of spatiotemporal dependencies with lightweight deployment requirements, enabling real-time air-traffic monitoring and early warning on standard CPUs and embedded devices. This framework offers a scalable solution for enhancing the operational safety and efficiency of modern air-traffic control systems.

To effectively predict the fuel consumption of civil aviation aircraft during their flight routes, enhance fuel economy, and promote energy conservation and emission reduction, we propose a hybrid model that combines adaptive noise complete empirical mode decomposition (CEEMDAN) with an improved arctic puffin optimization (IAPO) algorithm optimized convolutional long short-term memory neural network (CNN-LSTM). Initially, the Pearson coefficient is employed for correlation analysis to reduce the nine-dimensional factors influencing aircraft fuel consumption, transforming them into five principal components. Subsequently, CEEMDAN is utilized to decompose the original fuel consumption data, yielding more regular subsequences. Subsequently, the convolutional neural network (CNN) efficiently extracts data features, which are then input into the LSTM network. Furthermore, utilizing the SPM chaotic map strategy for population initialization. The introduction of the golden sine operator mutation strategy enhances the local search capability of the algorithm, while the adaptive dive switching strategy adjusts the search intensity, significantly improving the global search performance and convergence speed of the Arctic Puffin optimization algorithm. Ultimately, the multi-strategy improved Arctic Puffin Optimization algorithm is utilized to adaptively optimize the hyperparameters of the CNN-LSTM model, allowing for the superimposition of each subsequence to yield the final prediction result. Simulation results indicate that the CEEMDAN-IAPO-CNN-LSTM model outperforms other comparative models in terms of prediction accuracy and exhibits lower prediction error. This model presents a novel and efficient method for predicting fuel consumption in airlines, offering valuable insights for reducing aircraft fuel consumption.

Introduction: To address the challenges of cumulative errors, insufficient modeling of complex spatiotemporal features, and limitations in computational efficiency and generalization ability in 4D trajectory prediction, this paper proposes a high-precision, robust prediction method.Methods: A hybrid model SVMD-DBO-RCBAM is constructed, integrating sequential variational modal decomposition (SVMD), the dung beetle optimization algorithm (DBO), and the ResNet-CBAM network. Innovations include frequency-domain feature decoupling, dynamic parameter optimization, and enhanced spatio-temporal feature focusing.Results: Experiments show that the model achieves a low longitude MAE of 0.0377 in single-step prediction, a 38.5% reduction compared to the baseline model; in multi-step prediction, the longitude R2 reaches 0.9844, with a 72.9% reduction in cumulative error rate and an IQR of prediction errors less than 10% of traditional models, demonstrating high accuracy and stability.Discussion: Experiments show that the model achieves a low longitude MAE of 0.0377 in single-step prediction, a 38.5% reduction compared to the baseline model; in multi-step prediction, the longitude R2 reaches 0.9844, with a 72.9% reduction in cumulative error rate and an IQR of prediction errors less than 10% of traditional models, demonstrating high accuracy and stability.