Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity

Veena R S; Abhijna K C; B G Shreyas; Bhargavi; Dhanush Gowda S

Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity

May 2023

Vol-9, Issue-3

Paper ID: 20428

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Cyclone Intensity Convolutional Neural Network Keras Tensorflow Infrared

Abstract

This paper presents a novel approach for estimating cyclone intensity using deep learning techniques on images obtained from the INSAT 3D satellite. The deep learning algorithms used in this work to analyze photos from the INSAT 3D satellite to estimate cyclone strength are innovative. The convolutional neural network (CNN) is used in the proposed method to extract information from the photos and calculate the cyclone's strength. The collection of labelled images used to train the algorithm was created by combining data from ground-based observations with remote sensing. The findings demonstrate that the suggested method achieves excellent accuracy in cyclone intensity prediction and outperforms conventional methods for estimating cyclone intensity. Cyclone intensity estimation is essential for disaster management and early warning systems, and the suggested approach has the potential to greatly increase both its accuracy and speed.

License

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Author Information

#	Name	Institute / Affiliation
1	Veena R S	Dayananda Sagar Academy Of Technology And Management
2	Abhijna K C	Dayananda Sagar Academy Of Technology And Management
3	B G Shreyas	Dayananda Sagar Academy Of Technology And Management
4	Bhargavi	Dayananda Sagar Academy Of Technology And Management
5	Dhanush Gowda S	Dayananda Sagar Academy Of Technology And Management

How to Cite

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

APA Style

S, Veena R, C, Abhijna K, Shreyas, B G, Bhargavi, & S, Dhanush Gowda (2023). Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity. International Journal of Advance Research and Innovative Ideas In Education, 9(3), 2140-2145.

MLA Style

S, Veena R, et al. "Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, 2023, pp. 2140-2145.

IEEE Style

Veena R S, Abhijna K C, B G Shreyas, Bhargavi, and Dhanush Gowda S, "Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, pp. 2140-2145, 2023.

Vancouver Style

S Veena R, C Abhijna K, Shreyas B G, Bhargavi, S Dhanush Gowda. Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(3):2140-2145.

Harvard Style

S, Veena R, C, Abhijna K, Shreyas, B G, Bhargavi, & S, Dhanush Gowda (2023) 'Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity', International Journal of Advance Research and Innovative Ideas In Education, 9(3), pp. 2140-2145.

Chicago Style

S, Veena R, et al. "Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 2140-2145.

Turabian Style

S, Veena R, et al. "Deep Learning and INSAT-3D IR Imagery for Estimating Cyclone Intensity." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 2140-2145.