DEEP SPATIOTEMPORAL LEARNING FOR CYCLONE FORECASTING USING CONVOLUTIONAL LSTM AND DIVERSE CLIMATE INPUTS

Abstract

Tropical cyclones represent one of the most destructive natural phenomena, causing massive loss of life, property damage, and disruption to coastal ecosystems [7][18]. Accurate forecasting of cyclone formation, path, and intensity remains a complex challenge due to the nonlinear and dynamic nature of atmospheric systems [9][24]. Traditional numerical weather prediction (NWP) models often require immense computational resources and are limited in their ability to capture intricate spatial–temporal dependencies [24]. To overcome these challenges, this research proposes a deep spatiotemporal learning framework that integrates Convolutional Long Short-Term Memory (ConvLSTM) networks for cyclone forecasting [2][10][19]. The ConvLSTM model combines the spatial feature extraction capabilities of convolutional layers with the temporal learning strength of LSTM units [1][6], allowing it to effectively learn from historical climate patterns [15][16]. The system utilizes multi-source meteorological data, including satellite imagery, sea surface temperature (SST), air pressure, wind velocity, and relative humidity [12][18][25]. These diverse climate variables are synchronized, normalized, and preprocessed to ensure temporal alignment and noise reduction [7][13]. Through a series of experiments, the proposed ConvLSTM framework demonstrates its ability to accurately predict cyclone tracks and intensity progression over time [19][20]. Comparative analysis with baseline models, such as CNNs and standalone LSTM architectures [4][6][11], shows significant improvement in mean squared error (MSE) and prediction stability [14][15]. The proposed model captures complex atmospheric interactions that influence cyclone evolution, enabling early warning generation with improved precision [17][21]. Furthermore, it minimizes false alarm rates and enhances spatial resolution in cyclone path estimation [9][22]. This research also emphasizes model interpretability by visualizing learned spatial features and temporal transitions within the ConvLSTM layers [3][5][10]. Such visual insights contribute to a better understanding of the underlying meteorological processes [23]. The overall system provides an efficient, datadriven approach for short-term and medium-range cyclone forecasting [8][24]. Integration with real-time climate data sources can enable continuous updates and adaptive learning [20][25]. Results from this study suggest that deep spatiotemporal learning, supported by diverse climate inputs, can complement existing meteorological forecasting systems [9][11]. The proposed ConvLSTM framework offers a scalable, accurate, and cost-effective solution that strengthens disaster preparedness and management strategies [18][19]. In conclusion, this work highlights the potential of hybrid deep learning architectures in enhancing the reliability of climate prediction models [10][17], marking a step forward toward intelligent and automated weather forecasting solutions [15][25].