@inproceedings{555831f4b5f648218c2a428b87a563ad,
title = "ENSO-based Ensemble Learning Approach for Tropical Cyclone Intensity Estimation",
abstract = "Estimation of tropical cyclones (TCs) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using ensemble machine learning models. We utilized four Random Forest (RF) models to predict TC intensity. The input factors include TC's location, central pressure, distance to land, landfall in the next six-hour, storm speed, and storm direction, which are extracted from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). This dataset was divided into four sub-datasets based on the ENSO phases (Neutral, El Ni{\~n}o, and La Ni{\~n}a). Inputs for the RF models were taken from each sub-dataset separately. Results show that central pressure has the greatest effect on TC intensity estimate with the maximal root mean square error (RMSE) of 4.92 knots (equivalent to 2.50 m/s) and accuracy of 94-96 %.",
keywords = "ENSO, NWP, machine learning, random forest, tropical cyclone",
author = "Le, {Truong Vinh} and Liou, {Yuei An}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022 ; Conference date: 14-10-2022 Through 16-10-2022",
year = "2022",
doi = "10.1109/IET-ICETA56553.2022.9971544",
language = "???core.languages.en_GB???",
series = "Proceedings - 2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022",
}
