Tropical cyclone track prediction harnessing deep learning algorithms: A comparative study on the Northern Indian Ocean

Tropical Cyclones (TCs), among the most destructive natural phenomena, pose severe risks to coastal regions and maritime activities worldwide. Accurate cyclone track prediction is crucial for minimizing property damage, enhancing disaster preparedness, and safeguarding human lives. This study evaluates the efficacy of deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and conventional Recurrent Neural Network (RNN) in forecasting cyclone trajectories. Given the nonlinear and highly dynamic nature of cyclone movements, deep learning models offer a promising approach by effectively capturing long-term dependencies and processing sequential meteorological data. Utilizing an extensive dataset of historical cyclone tracks and atmospheric parameters, we systematically compare these models across multiple performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results demonstrate that LSTM consistently outperforms GRU and RNN, achieving the lowest RMSE (0.14 km), MAE (0.11 km), and MAPE (0.61%) for severe cyclones and maintaining high accuracy across various storm intensities. In contrast, GRU exhibits higher error rates, with RMSE reaching 1.97 km and MAPE increasing to 6.74% for intense storms, while RNN struggles significantly with RMSE exceeding 2.98 km and MAPE rising to 17.72% for extreme events. LSTM's ability to adapt to temporal variations and handle missing or noisy data makes it particularly well-suited for operational forecasting. Beyond model performance, this study highlights the practical implications of deploying LSTM in real-world meteorological systems. By integrating LSTM-based forecasting into operational frameworks, early warning systems can provide more precise and reliable cyclone track predictions, enabling improved disaster response, better resource allocation, and reduced economic losses. These findings underscore the transformative potential of deep learning in meteorology and disaster management, paving the way for future advancements in AI-driven forecasting techniques.