Abstract
Satellite imagery provides the initial data information in cyclone detection and forecasting. To mitigate the damages caused by cyclones, we have trained data augmentation and interpolation techniques for enhancing the time-related resolution and diversification of characters in a specific dataset. The algorithm needs classical techniques during pre-processing steps. Using 14 distinct constraint optimization techniques on three optical flow methods estimations are tested here internally. A Convolutional Neural Network learning model is trained and tested within artificially intensified and classified storm data for cyclone identification and locating the cyclone vortex giving minimum of 90% accuracy. The work analyzes two remote sensing data consisting of merged precipitation data from TRMM and QuikSCAT wind satellite data and other satellites for feature extraction. The result and analysis show that the methodology met the objectives of the project.
Supported by Jain (Deemed-to-be) University
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Kumawat, S., Jaiswal, J. (2021). Cyclone Detection and Forecasting Using Deep Neural Networks Through Satellite Data. In: Mandal, J.K., Mukhopadhyay, S., Unal, A., Sen, S.K. (eds) Proceedings of International Conference on Innovations in Software Architecture and Computational Systems. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-4301-9_2
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