Cyclone Intensity and Damage Estimation using Deep Learning

The goal of this study is to quantify cyclone damage and anticipate severity by using deep learning algorithms to analyze satellite data. First, raw satellite photos are gathered, and then a multispectral model is used to brighten them. A hybrid CNN (ViT architecture) and LSTM model are then used to process the improved images in order to extract features. A transformer model is used to improve temporal and geographical analysis, which results in more precise predictions of cyclone intensity. Real-time visualization for well-informed decision-making is made possible by the final output, which offers insights into wind speed, cyclone types, and anticipated damage zones. This method advances meteorology, disaster preparedness, and climate study. The model's forecasts can also help with emergency response resource allocation optimization. To improve prediction accuracy, future developments might use real-time weather data.