Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning
Abstract & Details
Research Area
Computer Engineering
Keywords
Satellite image classification
cyclone intensity prediction
satellite images
Deep Convolutional Neural Network (DCNN)
Insat 3D IR
metadata
Abstract
This survey paper is based on the review of Cyclone strength prediction utilizing INSAT-3D satellite photos, using review articles that were published from 2018 to 2022. A natural disaster is an unanticipated event that can harm the environment at any time and at any place. There are several natural disasters that cause harm to society and its citizens. disasters including earthquakes, cyclones, floods, tsunamis, wildfires, landslides, and volcanic eruptions.
Some of the frequent natural calamities include avalanches, heat waves, and many others. Cyclones are enormous masses of air that move counterclockwise in the Northern Hemisphere and clockwise in the Southern Hemisphere as they revolve around a powerful center of low atmospheric pressure. A cyclone is, in general, a large storm that produces heavy rain and gusts. Tropical cyclones, often known as typhoons or hurricanes, are extremely powerful, destructive, intense circular storms that develop over warm tropical oceans. INSAT is one of the numerous geostationary satellites owned by India. INSAT stands for Indian National Satellite System, and ISRO launched this multipurpose Geostationary satellite to meet India's demands for search and rescue operations as well as telecommunications and broadcasting. Including brightness temperatures of several IR channels, temperature and humidity profiles, atmospheric stability indices and parameters, precipitable water, geo-potential height, and many other variables, the INSAT 3D satellite accurately records cyclones and their evolution. This study's objective is to assess the cyclone's intensity utilizing the generated sequence of images.
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Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Abhijna K C | Dayananda Sagar Academy Of Technology And Management |
| 2 | B G Shreyas | Dayananda Sagar Academy Of Technology And Management |
| 3 | Bhargavi | Dayananda Sagar Academy Of Technology And Management |
| 4 | Dhanush Gowda S | Dayananda Sagar Academy Of Technology And Management |
| 5 | Dr. Madhumala R B | Dayananda Sagar Academy Of Technology And Management |
How to Cite
Use the following formats to cite this article in your research.
APA Style
C, Abhijna K, Shreyas, B G, Bhargavi, S, Dhanush Gowda, & B, Dr. Madhumala R (2023). Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning. International Journal of Advance Research and Innovative Ideas In Education, 9(1), 1695-1699.
MLA Style
C, Abhijna K, et al. "Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 1, 2023, pp. 1695-1699.
IEEE Style
Abhijna K C, B G Shreyas, Bhargavi, Dhanush Gowda S, and Dr. Madhumala R B, "Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 1, pp. 1695-1699, 2023.
Vancouver Style
C Abhijna K, Shreyas B G, Bhargavi, S Dhanush Gowda, B Dr. Madhumala R. Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(1):1695-1699.
Harvard Style
C, Abhijna K, Shreyas, B G, Bhargavi, S, Dhanush Gowda, & B, Dr. Madhumala R (2023) 'Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning', International Journal of Advance Research and Innovative Ideas In Education, 9(1), pp. 1695-1699.
Chicago Style
C, Abhijna K, et al. "Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning." International Journal of Advance Research and Innovative Ideas In Education 9, no. 1 (2023): 1695-1699.
Turabian Style
C, Abhijna K, et al. "Cyclone Intensity Estimation Using INSAT-3D IR Imagery and deep learning." International Journal of Advance Research and Innovative Ideas In Education 9, no. 1 (2023): 1695-1699.
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