Spatiotemporal prediction of satellite altimetry sea level anomalies in the tropical pacific ocean

Moslem Imani, Yi Ching Chen, Rey Jer You, Wen Hau Lan, Chung Yen Kuo, Jung Chieh Chang, Ashraf Rateb

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


This letter developes and validates a machine learning approach to forecast sea level anomalies (SLAs) derived from satellite altimetry in the tropical Pacific Ocean. The empirical orthogonal function (EOF), also known as principal component analysis, was used to extract dominant signals and reduce the dimensionality of data sets. Such dimensionality was decreased by describing spatial patterns (EOFs) and the corresponding temporal domains [principal components (PCs)]. Support vector regression (SVR) was employed to predict the time series obtained from the leading PCs. Thereafter, the temporal and spatial SLAs from the proposed EOFs were reconstructed to represent the spatiotemporal SLA prediction. Finally, the prediction result was compared with that of the conventional autoregressive integrated moving average (ARIMA) model. Both models reached satisfactory sea level predictions. Even so, intercomparison of the obtained results showed that the SVR significantly (P = 0.012) outperformed the ARIMA model in sea level forecasting. That is, a considerably low root-mean-square error was attained for the differences between the predicted and observed mean SLAs.

頁(從 - 到)1126-1130
期刊IEEE Geoscience and Remote Sensing Letters
出版狀態Published - 2017 7月

All Science Journal Classification (ASJC) codes

  • 岩土工程與工程地質
  • 電氣與電子工程


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