Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction

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

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題IoTBDS 2022 - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security
編輯Denis Bastieri, Gary Wills, Peter Kacsuk, Victor Chang
發行者Science and Technology Publications, Lda
頁面297-303
頁數7
ISBN(電子)9789897585647
DOIs
出版狀態Published - 2022
事件7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022 - Virtual, Online
持續時間: 22 4月 202224 4月 2022

出版系列

名字International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
2022-April
ISSN(電子)2184-4976

Conference

Conference7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022
城市Virtual, Online
期間22/04/2224/04/22

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