Cyclone Intensity Estimation Using Quantum Machine Learning

This research addresses the critical challenges of cyclone analysis and prediction, aiming to significantly advance the accuracy and efficiency of our understanding of cyclone behavior. Cyclones are powerful and destructive weather phe-nomena that can cause significant damage to life and property. Accurately predicting their intensity and potential impact is vital for timely evacuations and mitigation strategies. Current classical CNN models often struggle with classifying cyclones, mainly when their intensities exhibit subtle differences. Leveraging the NRLMRY (Naval Research Laboratory Monterey) Tropical Cyclone Data (TCDAT), we employ Quantum Machine Learning (QML) to enhance cyclone intensity estimation. By adopting a Quantum-Classical hybrid approach, which harnesses the power of quantum computing principles, we seek to overcome these limitations, providing more precise intensity estimations. Beyond intensity estimation, this project extends its focus to predicting the potential severity of damage that cyclones may inflict on cyclone-prone regions. Utilizing the same quantum-powered approach, we aspire to accurately classify whether a cyclone poses a significant threat to these vulnerable areas. Our holistic research endeavors to enhance the reliability of cyclone intensity estimation and damage prediction. Ultimately, integrating quantum machine learning and convolutional neural networks in cyclone analysis promises to contribute significantly to more effective disaster preparedness and response measures, safeguarding lives and property in cyclone-prone regions.