Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

Moslem Imani, Huan Chin Kao, Wen Hau Lan, Chung Yen Kuo

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Original languageEnglish
Pages (from-to)211-221
Number of pages11
JournalGlobal and Planetary Change
Volume161
DOIs
Publication statusPublished - 2018 Feb

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Oceanography

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