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Article

El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition

by 1,2, 1,2,*, 1,2 and 1,2
1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
College of Computer, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(6), 893; https://doi.org/10.3390/sym12060893
Received: 5 April 2020 / Revised: 12 May 2020 / Accepted: 15 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Optimized Machine Learning Algorithms for Modeling Dynamical Systems)
El Niño is an important quasi-cyclical climate phenomenon that can have a significant impact on ecosystems and societies. Due to the chaotic nature of the atmosphere and ocean systems, traditional methods (such as statistical methods) are difficult to provide accurate El Niño index predictions. The latest research shows that Ensemble Empirical Mode Decomposition (EEMD) is suitable for analyzing non-linear and non-stationary signal sequences, Convolutional Neural Network (CNN) is good at local feature extraction, and Recurrent Neural Network (RNN) can capture the overall information of the sequence. As a special RNN, Long Short-Term Memory (LSTM) has significant advantages in processing and predicting long, complex time series. In this paper, to predict the El Niño index more accurately, we propose a new hybrid neural network model, EEMD-CNN-LSTM, which combines EEMD, CNN, and LSTM. In this hybrid model, the original El Niño index sequence is first decomposed into several Intrinsic Mode Functions (IMFs) using the EEMD method. Next, we filter the IMFs by setting a threshold, and we use the filtered IMFs to reconstruct the new El Niño data. The reconstructed time series then serves as input data for CNN and LSTM. The above data preprocessing method, which first decomposes the time series and then reconstructs the time series, uses the idea of symmetry. With this symmetric operation, we extract valid information about the time series and then make predictions based on the reconstructed time series. To evaluate the performance of the EEMD-CNN-LSTM model, the proposed model is compared with four methods including the traditional statistical model, machine learning model, and other deep neural network models. The experimental results show that the prediction results of EEMD-CNN-LSTM are not only more accurate but also more stable and reliable than the general neural network model. View Full-Text
Keywords: El Niño index; deep learning; prediction; Long Short-Term Memory Neural Network; Convolutional Neural Network; Ensemble Empirical Mode Decomposition El Niño index; deep learning; prediction; Long Short-Term Memory Neural Network; Convolutional Neural Network; Ensemble Empirical Mode Decomposition
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MDPI and ACS Style

Guo, Y.; Cao, X.; Liu, B.; Peng, K. El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition. Symmetry 2020, 12, 893. https://doi.org/10.3390/sym12060893

AMA Style

Guo Y, Cao X, Liu B, Peng K. El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition. Symmetry. 2020; 12(6):893. https://doi.org/10.3390/sym12060893

Chicago/Turabian Style

Guo, Yanan, Xiaoqun Cao, Bainian Liu, and Kecheng Peng. 2020. "El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition" Symmetry 12, no. 6: 893. https://doi.org/10.3390/sym12060893

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