Research on Short- and Medium-Term Airport Visibility Prediction Based on the TimeGAN-GA-LSTM Model

Accurate airport visibility prediction is crucial for flight safety, as reduced visibility may lead to flight delays, cancellations, or even accidents, which impact the efficiency of air transportation. To improve prediction accuracy, this paper proposes a medium-to-short-term visibility prediction model based on TimeGAN-GA-LSTM. Using nearly 10 years of daily meteorological and pollutant data from Xiaoshan International Airport, feature selection is conducted through Spearman correlation analysis. The training dataset is enhanced using TimeGAN, and the LSTM hyperparameters are optimized using a Genetic Algorithm (GA). The results show that under the condition of a 1-day sliding window, the TimeGAN-GA-LSTM model achieves the best prediction accuracy for airport visibility. For a 580-day prediction dataset, the RMSE is 2.01, MAE is 1.49, and MAPE is 16%, which are reductions of 1.01, 0.72, and 15.2%, respectively, compared to the LSTM model, and the R^2 increases to 0.83. This study provides an effective method for intelligent airport visibility prediction, contributing to flight schedule optimization and safety enhancement