Convolution Neural Network Based Algorithm for Estimating the Intensity of Tropical Cyclone from Infrared Satellite Images


  • Shiv Mehta Dhirubhai Ambani International School
  • Reetu Jain Chief Mentor & Founder On My Own Technology Pvt Ltd


Unsupervised Machine Learning, Convolution Neural Network (Cnn), Satellite Imagery, Tropical Cyclones, Intensity Prediction


The Comprehensive Intention Of The Present Study Is To Develop A Convolution Neural Network (Cnn) Based Prediction Model To Estimate The Intensity Of Tropical Cyclone From The Infrared Satellite Imagery. The Proposed Methodology Is A Two Phase Unsupervised Machine Learning Algorithm. The First Phase Is A Data Acquisition Part That Extracts Data From The Hursat - B1 And Hurdat2 Satellites And Merges Them. The Second Phase Involves Converting The Image Matrix Into Convolved Matrix Which Is Then Passed Through A Rectified Linear Unit Layer To Extract The Important Information From The Images. Finally The Information Are Passed Through A Fully Connected Layer Comprising Of Number Of Neurons That Are Connected To The Previous Layer. In The Proposed Model, Rms prop Optimizer Is Used to Get the Optimized Configuration. At Last, The Proposed Model Is Validated With Pictures Of 10 Different Tropical Cyclones And The Mean Absolute Error And Root Mean Square Error Computed Is 8.42 Knots And 11.14 Knots Respectively Which Is Well Within The Acceptable Range.


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How to Cite

Shiv Mehta, & Reetu Jain. (2022). Convolution Neural Network Based Algorithm for Estimating the Intensity of Tropical Cyclone from Infrared Satellite Images. iJournals:International Journal of Software & Hardware Research in Engineering ISSN:2347-4890, 10(3). Retrieved from