التفاصيل البيبلوغرافية
| العنوان: |
Tropical cyclone prediction using visual geometry group (VGG) 16 model. |
| المؤلفون: |
Pathinettampadian, Karthikeyan, Paramasivam, Saravana Kumar, Qayyum, Mohammed Abdul, Prakasam, Maya Jegadish, Srinivasan, Raghuraman, Mandankandy, Arun Anoop |
| المصدر: |
AIP Conference Proceedings; 2024, Vol. 3112 Issue 1, p1-8, 8p |
| مصطلحات موضوعية: |
DEEP learning, CYCLONE forecasting, CONVOLUTIONAL neural networks, TROPICAL cyclones, VERTICAL wind shear, TROPICAL storms, OCEAN temperature |
| مستخلص: |
Deep convolutional neural network (CNN) models were developed in this research using data from the Best Track of tropical storms, as well as ocean and atmospheric reanalysis, to forecast stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum two-min mean wind speed (MWS). Sensitivity tests were developed to assess the model structure's interpretability by testing various combinations of predictors. The results of the model test indicated that the simplified VGG-16 (VGG-16 s) model performed better than the other two general models (Le Net-5 and Alex Net). The sensitivity studies, which supported the premise and perceptions, validated the validity and reliability of the model. According to the research, the significance of predictors changed depending on the goal. The correlations between vertical wind shear speed (VWSS), temperature at 500 hPa (TEM_500), and sea surface temperature (SST) were quite high, while MCP was less significant for TCI. More significant than other variables for MWS and SST were the temperatures at 500 hPa (TEM_500) and 850 hPa (TEM_850). As per the study's conclusions, deep learning might be an alternative approach to conduct comprehensive, quantitative research on tropical cyclones. Deep learning and convolutional neural networks were used in the research to forecast tropical cyclones, and the interpretability of the results was examined. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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