Leveraging convlstm and satellite imagery for predictive modeling of floods, landslides, and earthquakes

This study combines the spatial data from satellite imagery with the temporal learning capabilities of convolutional long short-term memory (ConvLSTM) networks to improve both prediction accuracy and processing efficiency. By utilizing diverse spectral bands and resolutions, the model captures a wide range of environmental features. Preprocessing steps, such as normalization and noise reduction, are applied to refine the input data and enhance the performance of the ConvLSTM network. The architecture is carefully structured to balance spatial and temporal dependencies, ensuring the effective integration of satellite-derived data. The framework is optimized to identify complex relationships within the dataset, enabling precise forecasts of upcoming disasters. It has been tested on various natural events, including hurricanes, floods, and wildfires, achieving higher prediction accuracy and shorter lead times compared to traditional techniques. This integration of satellite imagery with ConvLSTM networks aims to strengthen early warning systems, improve disaster preparedness, and reduce economic and social damage to affected regions.