Aircraft range fuel prediction study based on WPD with IAPO optimized BiLSTM-KAN model.

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 (R) 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 R of this model are 40.71, 0.0078, 2.61, and 0.9934, respectively. In the B747 aircraft model, the MSE, NRMSE, MAPE, and R 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.