Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIoTBDS 2022 - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security
EditorsDenis Bastieri, Gary Wills, Peter Kacsuk, Victor Chang
PublisherScience and Technology Publications, Lda
Pages297-303
Number of pages7
ISBN (Electronic)9789897585647
DOIs
Publication statusPublished - 2022
Event7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022 - Virtual, Online
Duration: 22 Apr 202224 Apr 2022

Publication series

NameInternational Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
Volume2022-April
ISSN (Electronic)2184-4976

Conference

Conference7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022
CityVirtual, Online
Period22/04/2224/04/22

Keywords

  • Ensemble Machine Learning Algorithm
  • Machine Learning
  • Natural Disaster
  • Storm Surge

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