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Title: :  DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY
PaperId: :  21704
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 5 2023
DUI:    16.0415/IJARIIE-21704
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
MUHAMED ABID SBANNARI AMMAN INSTITUE OF TECHNOLOGY
SUSHMA VBANNARI AMMAN INSTITUE OF TECHNOLOGY
RANJITH KUMAR PBANNARI AMMAN INSTITUE OF TECHNOLOGY
GAYATHRI KBANNARI AMMAN INSTITUE OF TECHNOLOGY

Abstract

Artificial Intelligence
Deep learning research, Intensity, Estimation, and less timing complexity
Classifying the severity of a given cyclone is one of the key aspects of cyclone forecasting. The risk to human lives and the harm the storm does to the environment can both be decreased by projecting the strength of the cyclone. The Dvorak approach has traditionally been used to estimate cyclone intensity. The technique's highlights on the analysis of the cyclone's cloud patterns poses one of its biggest difficulties. By automating the intensity estimation procedure and eliminating the distinction involved with manual analysis, CNNs and INSAT 3D images significantly contribute to resolving these difficulties. It is a diagnostic model since it can properly predict the intensity of tropical cyclones. Estimating the disaster's intensity is a crucial step for staying updated on it. The primary goal of this research project is to estimate cyclone intensity in order to prevent damage from cyclones, which can be quite dangerous. The identification of previously unrecognized patterns in the existence of cyclone intensity and irregularities in earlier observations. This might make it easier for people to understand how tropical cyclone intensity changes. We used satellite photography to find the tropical cyclone because of the ideology. The technology uses deep learning research with hurricane satellite data to provide an automated way for cyclone estimation. The model is fine-tuned to take into account a range of environmental factors, such as the state of the air and sea surface temperatures, that have an impact on cyclone strength. In the research, the deep learning model outperformed more traditional approaches in terms of cyclone intensity forecast accuracy, which is encouraging. The current system needs more time complexity for accurate evaluations of tropical cyclone strength.

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IJARIIE MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. "DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1140-1144
MLA MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. "DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1140-1144.
APA MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, & GAYATHRI K. (2023). DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1140-1144.
Chicago MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. "DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1140-1144.
Oxford MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. 'DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1140-1144. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/DEEP_LEARNING_BASED_CYCLONE_INTENSITY_ESTIMATION_USING_INSAT_3D_IR_IMAGERY_ijariie21704.pdf (Accessed : 31 March 2024).
Harvard MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. (2023) 'DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1140-1144IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/DEEP_LEARNING_BASED_CYCLONE_INTENSITY_ESTIMATION_USING_INSAT_3D_IR_IMAGERY_ijariie21704.pdf (Accessed : 31 March 2024)
IEEE MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K, "DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1140-1144, Sep-Oct 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/DEEP_LEARNING_BASED_CYCLONE_INTENSITY_ESTIMATION_USING_INSAT_3D_IR_IMAGERY_ijariie21704.pdf [Accessed : 31 March 2024].
Turabian MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. "DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 (31 March 2024).
Vancouver MUHAMED ABID S, SUSHMA V, RANJITH KUMAR P, and GAYATHRI K. DEEP LEARNING BASED CYCLONE INTENSITY ESTIMATION USING INSAT-3D IR IMAGERY. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 31 March 2024]; 9(5) : 1140-1144. Available from: https://ijariie.com/AdminUploadPdf/DEEP_LEARNING_BASED_CYCLONE_INTENSITY_ESTIMATION_USING_INSAT_3D_IR_IMAGERY_ijariie21704.pdf
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