Tropical Cyclone Track Prediction in the TCWC Indonesia Monitoring Area Using Deep Recurrent Neural Networks
Received: 3 June 2025 | Revised: 4 September 2025 | Accepted: 11 September 2025 | Online: 8 December 2025
Corresponding author: Ida Pramuwardani
Abstract
Tropical Cyclones (TCs) are rapidly rotating large-scale storm systems and rank as the second most destructive natural hazards after earthquakes. Disaster mitigation in TC-prone regions is critical, particularly in view of the two-fold increase in population in these areas over the past five decades. This research focuses on the Area of Monitoring (AoM) in Indonesia for TC, which spans from 20°N to 20°S latitude and from 90°E to 141°E longitude. The dataset is sourced from the International Best Track Archive for Climate Stewardship (IBTrACS), filtered to include only TCs that occur in this AoM region. The preprocessing involved using a sliding window with a sequence length of three to generate input features. Four Recurrent Neural Network (RNN) models were evaluated: Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). All models were trained using incremental learning, comprising 158 iterations for the northern AoM region and 69 iterations for the southern AoM region. The evaluation results indicate that the LSTM model has high performance, with an average Mean Absolute Error (MAE) of 0.24485 degrees in the northern region and 0.22330 degrees in the southern region.
Keywords:
tropical cyclone, track prediction, deep learning, recurrent neural network, disaster mitigationDownloads
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Copyright (c) 2025 Fakhrul Alam, Gede Putra Kusuma, Ida Pramuwardani
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