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Prediction of sea surface temperature in the tropical Atlantic by support vector machines

Author

Listed:
  • Lins, Isis Didier
  • Araujo, Moacyr
  • Moura, Márcio das Chagas
  • Silva, Marcus André
  • Droguett, Enrique López

Abstract

The Sea Surface Temperature (SST) is one of the environmental indicators monitored by buoys of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) Project. In this work, a year-ahead prediction procedure based on SST knowledge of previous periods is proposed and coupled with Support Vector Machines (SVMs). The proposed procedure is focused on seasonal and intraseasonal aspects of SST. Data from PIRATA buoys are used in various ways to feed the SVM models: with raw data, using information about the SST slopes and by means of SST curvatures. The influence of these data handling strategies over the predictive capacity of the proposed methodology is discussed. Additionally, the forecasts’ accuracy is evaluated as the number of years considered in the SVM training phase increases. The raw data and the curvatures presented quite similar performances, they are more efficient than the slopes; the respective Mean Absolute Percentage Error (MAPE) values do not exceed 2% and all Mean Absolute Errors (MAEs) are lower than 0.37 °C. Besides, as the number of years considered in the training set increases, the MAPE and MAE values tend to stabilize.

Suggested Citation

  • Lins, Isis Didier & Araujo, Moacyr & Moura, Márcio das Chagas & Silva, Marcus André & Droguett, Enrique López, 2013. "Prediction of sea surface temperature in the tropical Atlantic by support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 187-198.
    • Handle: RePEc:eee:csdana:v:61:y:2013:i:c:p:187-198
    DOI: 10.1016/j.csda.2012.12.003
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    References listed on IDEAS

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    1. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
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    Cited by:

    1. Jiahao Shi & Jie Yu & Jinkun Yang & Lingyu Xu & Huan Xu, 2022. "Time Series Surface Temperature Prediction Based on Cyclic Evolutionary Network Model for Complex Sea Area," Future Internet, MDPI, vol. 14(3), pages 1-16, March.
    2. Gonzalo Astray & Benedicto Soto & Enrique Barreiro & Juan F. Gálvez & Juan C. Mejuto, 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
    3. Heitor de Oliveira Duarte & Enrique Lopez Droguett & Márcio das Chagas Moura & Elainne Christine de Souza Gomes & Constança Barbosa & Verônica Barbosa & Moacyr Araújo, 2014. "An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: The Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil," Risk Analysis, John Wiley & Sons, vol. 34(5), pages 831-846, May.
    4. Lins, Isis Didier & Droguett, Enrique López & Moura, Márcio das Chagas & Zio, Enrico & Jacinto, Carlos Magno, 2015. "Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 120-128.
    5. Qi He & Cheng Zha & Wei Song & Zengzhou Hao & Yanling Du & Antonio Liotta & Cristian Perra, 2020. "Improved Particle Swarm Optimization for Sea Surface Temperature Prediction," Energies, MDPI, vol. 13(6), pages 1-18, March.
    6. Hossein Kamalzadeh & Saeid Nassim Sobhan & Azam Boskabadi & Mohsen Hatami & Amin Gharehyakheh, 2019. "Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines," Papers 1912.02373, arXiv.org.

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