Volume IV-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 101-105, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 101-105, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2017

19 Oct 2017

Leveraging LSTM for rapid intensifications prediction of tropical cyclones

Y. Li, R. Yang, C. Yang, M. Yu, F. Hu, and Y. Jiang Y. Li et al.
  • 1Department of Geography & Geoinformation Science, George Mason University, 4400 University Drive, Fairfax, VA, USA

Keywords: rapid intensification prediction, tropical cyclones, LSTM, deep learning, class imbalanced problem

Abstract. Tropical cyclones (TCs) usually cause severe damages and destructions. TC intensity forecasting helps people prepare for the extreme weather and could save lives and properties. Rapid Intensifications (RI) of TCs are the major error sources of TC intensity forecasting. A large number of factors, such as sea surface temperature and wind shear, affect the RI processes of TCs. Quite a lot of work have been done to identify the combination of conditions most favorable to RI. In this study, deep learning method is utilized to combine conditions for RI prediction of TCs. Experiments show that the long short-term memory (LSTM) network provides the ability to leverage past conditions to predict TC rapid intensifications.