Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN

KANNAN T; HEMANTH V R; SARAN S; Suseela D

Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN

October 2023

Vol-9, Issue-5

Paper ID: 21745

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science Engineering

Keywords

Cyclone Intensity Prediction Disaster Management Deep Learning Convolutional Neural Networks(CNN) Recurrent Neural Networks(RNN) Disaster Preparedness.

Abstract

Cyclone intensity prediction stands as a pivotal facet of disaster management, carrying profound implications for the successful execution of disaster mitigation strategies. This research embarks on an exploration of the profound potential residing within deep learning methodologies, with a particular focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to propel the field of cyclone intensity estimation forward. Leveraging a vast and meticulously curated dataset, which includes a wealth of meteorological measurements and historical cyclone data, our methodology orchestrates a synergy between spatial feature extraction, skill fully executed by CNNs, and the precision of temporal analysis, orchestrated by RNNs. The findings unveiled by this study underscore the unwavering efficacy of deep learning models in profoundly elevating the accuracy of cyclone intensity forecasts, echoing a clarion call for their robust integration into disaster preparedness and response strategies, ultimately fostering resilience in the face of cyclonic events.

License

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Author Information

#	Name	Institute / Affiliation
1	KANNAN T	Bannari Amman Institute of Technology, Tamil Nadu, India
2	HEMANTH V R	Bannari Amman Institute of Technology, Tamil Nadu, India
3	SARAN S	Bannari Amman Institute of Technology, Tamil Nadu, India
4	Suseela D	Bannari Amman Institute of Technology, Tamil Nadu, India

How to Cite

Use the following formats to cite this article in your research.

APA Style

T, KANNAN, R, HEMANTH V, S, SARAN, & D, Suseela (2023). Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN. International Journal of Advance Research and Innovative Ideas In Education, 9(5), 1333-1338.

MLA Style

T, KANNAN, et al. "Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 5, 2023, pp. 1333-1338.

IEEE Style

KANNAN T, HEMANTH V R, SARAN S, and Suseela D, "Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 5, pp. 1333-1338, 2023.

Vancouver Style

T KANNAN, R HEMANTH V, S SARAN, D Suseela. Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(5):1333-1338.

Harvard Style

T, KANNAN, R, HEMANTH V, S, SARAN, & D, Suseela (2023) 'Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN', International Journal of Advance Research and Innovative Ideas In Education, 9(5), pp. 1333-1338.

Chicago Style

T, KANNAN, et al. "Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN." International Journal of Advance Research and Innovative Ideas In Education 9, no. 5 (2023): 1333-1338.

Turabian Style

T, KANNAN, et al. "Deep Learning Based Cyclone Intensity Estimation Using CNN and RNN." International Journal of Advance Research and Innovative Ideas In Education 9, no. 5 (2023): 1333-1338.