loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Vai-Kei Ian 1 ; Rita Tse 2 ; 1 ; Su-Kit Tang 2 ; 1 and Giovanni Pau 3 ; 1 ; 4

Affiliations: 1 Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao SAR, China ; 2 Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao SAR, China ; 3 Department of Computer Science and Engineering - DISI, University of Bologna, Via Zamboni, 33, 40126 Bologna, Italy ; 4 UCLA Computer Science Department, 404 Westwood Plaza, Los Angeles, CA, U.S.A.

Keyword(s): Storm Surge, Machine Learning, Ensemble Machine Learning Algorithm, Natural Disaster.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.210.83.20

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - IoTBDS; ISBN 978-989-758-564-7; ISSN 2184-4976, SciTePress, pages 297-303. DOI: 10.5220/0011109400003194

@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 - IoTBDS},
year={2022},
pages={297-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011109400003194},
isbn={978-989-758-564-7},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction
SN - 978-989-758-564-7
IS - 2184-4976
AU - Ian, V.
AU - Tse, R.
AU - Tang, S.
AU - Pau, G.
PY - 2022
SP - 297
EP - 303
DO - 10.5220/0011109400003194
PB - SciTePress