Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction

Vai-Kei Ian, Rita Tse, Rita Tse, Su-Kit Tang, Su-Kit Tang, Giovanni Pau, Giovanni Pau, Giovanni Pau

2022

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.

Paper Citation

in Harvard Style

Ian V., Tse R., Tang S. and Pau G. (2022). Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction. In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-564-7, pages 297-303. DOI: 10.5220/0011109400003194

in Bibtex Style

@conference{iotbds22,
author={Vai-Kei Ian and Rita Tse and Su-Kit Tang and Giovanni Pau},
title={Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction},
booktitle={Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2022},
pages={297-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011109400003194},
isbn={978-989-758-564-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction
SN - 978-989-758-564-7
AU - Ian V.
AU - Tse R.
AU - Tang S.
AU - Pau G.
PY - 2022
SP - 297
EP - 303
DO - 10.5220/0011109400003194