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Estimating Cyclone Intensity Using INSAT-3D IR Images Based on Convolution Neural Network Model
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<div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87356</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Estimating Cyclone Intensity Using INSAT-3D IR Images Based on Convolution Neural Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divvela%20Vishnu%20Sai%20Kumar">Divvela Vishnu Sai Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Arora"> Deepak Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheenu%20Rizvi"> Sheenu Rizvi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts. <iframe src="https://publications.waset.org/abstracts/163095.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimating%20cyclone%20intensity" title="estimating cyclone intensity">estimating cyclone intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title=" convolution neural network"> convolution neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20models" title=" prediction models"> prediction models</a> </p> <a 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