<![CDATA[Frequent thunderstorm activity around Supadio Airport, Pontianak, highlights the need for reliable lightning forecasting to support aviation safety and airport operations. In practice, most lightning systems are still used for detection rather than prediction, while many previous forecasting studies have relied on a single deep learning model, which may limit the ability to capture temporal patterns in meteorological data. Therefore, this study applied a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to predict lightning occurrences using historical meteorological data from Supadio Airport. The main contribution of this study lies in the development of a hybrid LSTM–GRU framework for airport-scale lightning prediction using station-based historical meteorological data. This setting has received limited attention in previous studies and combined LSTM and GRU within a single framework to improve sequence learning while maintaining computational efficiency, in contrast to previous single-model approaches. The experimental results show that the proposed model achieved a testing accuracy of 0.6716, with an F1-score of 0.71 and a Recall of 0.78 for the dominant lightning class. Although the model still showed limited performance in detecting rare lightning events due to class imbalance, the overall results indicate that the LSTM–GRU model has strong potential as a basis for an airport-scale early warning system and may help support safer, more reliable flight operations.]]>
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