Cyclone Detection
The comprehensive research project is aimed at leveraging self-supervised deep learning techniques to enhance cyclone intensity classification and improve disaster management in the fields of geological and climate sciences. The project will involve the development of advanced Scientific Machine Learning models, and collaboration with the Indian Space Research Organisation (ISRO) to analyze meteorological data. The ultimate goal is to provide accurate predictions, enable effective decision-making, and contribute to scientific advancements in cyclone analysis and disaster mitigation. The increasing frequency and intensity of cyclonic events demand more accurate and timely predictions to enhance disaster management efforts. This research project aims to utilize the power of ML techniques to improve cyclone intensity classification, cyclone genesis forecast and cyclone tracking, enabling effective mitigation strategies in geological and climate sciences. Collaboration with ISRO will provide access to rich meteorological data for analysis and validation.
Akash Agrawal, Mayesh Mohapatra, Paritosh Tiwari, Vishwajeet Pattanaik, Punit Rathore
Self-Supervised Learning, Time-series forecasting, Explainable AI
Satellite Imagery, INSAT 3D IR
Indian Space Research Organisation (ISRO)