INTENSITY ESTIMATION OF TROPICAL CYCLONE

Computer engineering

In recent times, the escalating frequency and intensity of tropical cyclones have emphasized the pressing need for advancements in cyclone monitoring and forecasting. Traditional methodologies often fall short in delivering precise intensity estimates, leaving vulnerable regions exposed to increased risks. In response to this challenge, our research proposes a groundbreaking approach that integrates state-of-the-art deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Trained meticulously on a preprocessed dataset of INSAT-3D Infrared (IR) imagery, our model leverages the spatial and temporal information inherent in these images, offering a robust framework for accurate and timely cyclone intensity predictions. This study addresses a significant gap in conventional methodologies by harnessing the power of deep learning for enhanced predictive capabilities, contributing to the evolution of cyclone monitoring.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.