Predicting Indian Ocean Cyclone Parameters Using an Artificial Intelligence Technique

Autor:	C. Purna Chand, M.M. Ali, Borra Himasri, Mark A. Bourassa, Yangxing Zheng
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	cyclone prediction artifical neural networks land crossing point mean distance error scatter index Meteorology. Climatology QC851-999
Zdroj:	Atmosphere, Vol 13, Iss 7, p 1157 (2022)
Druh dokumentu:	article
ISSN:	13071157 2073-4433
DOI:	10.3390/atmos13071157
Popis:	Precise prediction of a cyclone track with wind speed, pressure, landfall point, and the time of crossing the land are essential for disaster management and mitigation, including evacuation processes. In this paper, we use an artificial neural network (ANN) approach to estimate the cyclone parameters. For this purpose, these parameters are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS), from the National Oceanic and Atmospheric Administration (NOAA). Since ANN benefits from a large number of data points, each cyclone track is divided into different segments. We use past information to predict the geophysical parameters of a cyclone. The predicted values are compared with the observations.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/0996ae3f12fa4a1690e752bcff024131 Zobrazit plný text záznamu View record in DOAJ