Visibility Prediction Using Long Short-Term Memory on Multivariate Spatio-Temporal Data

Abstract

Visibility prediction at airports is a crucial factor in maintaining flight safety and operational efficiency. This study aims to develop a visibility prediction model at Juanda International Airport by utilizing multivariate Automatic Weather Observation System (AWOS) data based on the Long Short-Term Memory (LSTM) approach. The dataset consists of one-minute weather observations throughout 2022 from three observation points, namely point 10, point 10–28, and point 28. The research process includes data preprocessing, missing value imputation using continuous and vector interpolation, dimensionality reduction with Principal Component Analysis (PCA), sequence formation of 168 time steps, and training of a two-layer LSTM model with dropout regularization. The evaluation results show that the model is able to provide accurate and stable predictions, with a Mean Absolute Error (MAE) of 0.0617 on validation data and 0.0618 on test data. Meanwhile, the Mean Squared Error (MSE) obtained is 0.0170 on validation data and 0.0150 on test data, while the Root Mean Square Error (RMSE) is 0.1305 on validation data and 0.1225 on test data. The small differences between validation and test results confirm the good generalization capability of the model without significant overfitting, while the use of PCA has proven effective in enhancing performance by highlighting the most relevant features. Overall, this study demonstrates that the multivariate LSTM approach using AWOS data is effective for supporting automatic visibility prediction systems at airports and opens opportunities for the development of data-driven early warning systems.