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Open AccessArticle

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

1
Department of Civil Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(3), 207; https://doi.org/10.3390/e20030207
Received: 1 February 2018 / Revised: 13 March 2018 / Accepted: 15 March 2018 / Published: 20 March 2018
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering)
Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN) modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs) of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment) for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR) model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size. View Full-Text
Keywords: entropy ensemble filter; ensemble model simulation criterion; EEF method; bootstrap aggregating; bagging; bootstrap neural networks; El Niño; ENSO; neural network forecast; sea surface temperature; tropical Pacific entropy ensemble filter; ensemble model simulation criterion; EEF method; bootstrap aggregating; bagging; bootstrap neural networks; El Niño; ENSO; neural network forecast; sea surface temperature; tropical Pacific
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MDPI and ACS Style

Foroozand, H.; Radić, V.; Weijs, S.V. Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures. Entropy 2018, 20, 207.

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