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천리안 위성 자료를 활용한 합성곱 순환 신경망 기반 태풍 최대풍속 산출Predicting Maximum Wind Speed of Typhoons based on Convolutional Recurrent Neural Network via COMS Satellite Data
- Other Titles
- Predicting Maximum Wind Speed of Typhoons based on Convolutional Recurrent Neural Network via COMS Satellite Data
- Issue Date
- 2019
- Publisher
- 대한산업공학회
- Keywords
- Satellite; Typhoon; Intensity Estimation; Deep Learning; Convolutional Neural Network; Recurrent Neural Network
- Citation
- 대한산업공학회지, v.45, no.4, pp.349 - 360
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 45
- Number
- 4
- Start Page
- 349
- End Page
- 360
- ISSN
- 1225-0988
- Abstract
- It is crucial to predict the intensity of typhoons since they cause massive casualties and damage in property. Wepropose a model for estimating the maximum wind speed of typhoons using Convolutional Recurrent NeuralNetwork (CRNN). Compared to the current method in investigating typhoons which is fully subjected to themeteorologist’s analyzing skill and domain knowledge, the proposed model assists meteorologists to obtain theobjective analysis of typhoons. In previous studies, they construct the model utilizing only CNN. However, oursuggested model is built with CNN followed by LSTM to consider the fact that the typhoons occur sequentially. We train the model by using each single channel in COMS satellite data composed of IR1, IR2, WV, and SWIR. As a result, the CRNN model trained on WV shows the lowest RMSE error, which is .
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Related Researcher
- Han, Sung Won
- 공과대학 (School of Industrial and Management Engineering)