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.