@inproceedings{b07551ef628048f1b94533537b2fa712,
title = "Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction",
abstract = "Storm surge has recently emerged as a major concern. In case it occurs, we suffer from the damages it creates. To predict its occurrence, machine learning technology can be considered. It can help ease the damages created by storm surge, by predicting its occurrence, if a good dataset is provided. There are a number of machine learning algorithms giving promising results in the prediction, but using different dataset. Thus, it is hard to benchmark them. The goal of this paper is to examine the performance of machine learning algorithms, either single or ensemble, in predicting storm surge. Simulation result showed that ensemble algorithms can efficiently provide optimal and satisfactory result. The accuracy of prediction reaches a level, which is better than that of single machine learning algorithms.",
keywords = "Ensemble Machine Learning Algorithm, Machine Learning, Natural Disaster, Storm Surge",
author = "Ian, \{Vai Kei\} and Rita Tse and Tang, \{Su Kit\} and Giovanni Pau",
note = "Publisher Copyright: Copyright {\textcopyright} 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.; 7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022 ; Conference date: 22-04-2022 Through 24-04-2022",
year = "2022",
doi = "10.5220/0011109400003194",
language = "English",
series = "International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings",
publisher = "Science and Technology Publications, Lda",
pages = "297--303",
editor = "Denis Bastieri and Gary Wills and Peter Kacsuk and Victor Chang",
booktitle = "IoTBDS 2022 - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security",
}