Enhancing Cyclonic Intensity Identification through Deep Learning Methodology

Abstract

Deep learning-based approaches for estimating cyclone intensity aim to improve early warning systems, enabling authorities to take timely action and protect at-risk populations and critical infrastructure. The goal is to reduce the widespread destruction caused by cyclones globally. However, accurately predicting cyclone intensity remains difficult due to the highly unpredictable and rapidly evolving characteristics of these storms. Conventional techniques often fail to detect sudden shifts in intensity, highlighting the need for more advanced solutions. This research  project uses Convolutional Neural Networks (CNNs) to process satellite imagery and assess cyclone intensity in real-time, providing early warnings to communities and authorities. Here automation of  the detection of critical features in storm systems is done. CNN used here is to classify cyclones intensity which avoids human intervention, and reduces subjectivity and errors. The model is trained on diverse datasets which analyses cloud patterns and storm characteristics. This model also enables timely alerts that improve disaster preparedness. This method performs better than traditional, manual techniques by offering a faster, more reliable solution for cyclone monitoring. Our system’s real-team capability strengths disaster management, potentially saving lives and minimizing damage in regions prone to severe weather events.