Predicting Flight Delays Using LSTM and BILSTM Models with Shap Interpretation

Flight delays represent a critical challenge in air transportation, affecting passenger satisfaction, operational efficiency, and financial outcomes. This study develops predictive models for flight delay duration at Juanda International Airport, Surabaya, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks integrated with a Shapley Additive Explanations (SHAP) interpretability method. The research utilized 242,638 flight observations spanning January 2023 to October 2025, incorporating flight operational and meteorological variables. The dataset was partitioned into training (62.35%), validation (9.01%), and testing (28.64%) subsets. After Min-Max normalization and preprocessing, models were designed with varying hyperparameters through grid search optimization. Performance evaluation employed Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results demonstrated that LSTM with two hidden layers and sixteen neurons achieved superior performance, with an MAE of 15.657 minutes and an RMSE of 20.859 minutes on test data, slightly outperforming BiLSTM (MAE of 16.705 minutes and RMSE of 21.709 minutes), establishing LSTM as the optimal model. SHAP interpretation revealed that operational factors, particularly the association with major airports and routes (Jakarta and Surabaya), flight type, and airline, represent the dominant predictors of delays, whereas meteorological factors such as wind speed and temporal factors such as scheduling time have a relatively minor effect. Although the model's predictive power remains limited, this research provides valuable interpretability insights into delay determinants, enabling data-driven decision-making for airport management and airlines to enhance punctuality and operational efficiency.