Advanced Techniques for Estimation of Tropical Cyclone Intensity Forecasting using Deep Learning with INSAT-3D Satellite Imagery
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Advanced Techniques for Estimation of Tropical Cyclone Intensity Forecasting using Deep Learning with INSAT-3D Satellite Imagery
Dr. R.Santosh Kumar ¹, Srinivas Ramavath2, Uppari Mahesh3
¹²³⁴⁵Vasavi College of Engineering, Hyderabad, Telangana, India
santosh@staff.vce.ac.in, srinuramavath3656@gmail.com, upparimaheshsagar2427@gmail.com
Abstract
Tropical cyclones around the world represent considerable dangers for coastal regions and maritime operations. Their accurate intensity prediction remains crucial for effective disaster preparedness and response. Traditional intensity estimation methodologies primarily rely on manual analysis of satellite imagery, which introduces challenges of subjectivity and inefficiency. This research explores the application of contemporary deep learning methodologies for automating cyclone intensity prediction using INSAT-3D infrared imagery. We investigate various neural network architectures optimized for meteorological image processing, with particular emphasis on convolutional neural networks (CNNs). Our comprehensive evaluation demonstrates the efficacy of these models in extracting relevant features from satellite data and generating accurate intensity predictions. The automated framework developed in this study offers potential improvements in prediction accuracy, consistency, and operational efficiency for weather forecasting agencies.
This paper provides a systematic overview of the power metrics of methods, implementation strategies, and deep learning approaches when estimating tropical cyclone strength.
Keywords: Tropical Cyclone Analysis, Deep Neural Networks, Computer Vision in Meteorology, Satellite Image Processing, Disaster Prediction Systems
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