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

Guo, Yanan; Cao, Xiaoqun; Liu, Bainian; Peng, Kecheng

doi:10.3390/sym12060893

Cited by 32 publications

(18 citation statements)

References 50 publications

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“…Their model's accuracy fell for longer lead times, especially during the El Niño and La Niña periods. Guo et al [44] used long short-term memory network (LSTM) to predict ONI as a time series based on its previous values, after filtering out the noise using ensemble empirical mode decomposition. The LSTM requires each data sample to be vectorized.…”

Section: Related Workmentioning

confidence: 99%

Forecasting El Niño and La Niña Using Spatially and Temporally Structured Predictors and a Convolutional Neural Network

Hashemi

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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El Niño-Southern Oscillation (ENSO) is characterized by large-scale fluctuations of sea surface temperature in the central and eastern tropical Pacific accompanied by changes in the atmospheric circulation. ENSO events are of two main types: El Niño and La Niña. Oceanic Niño index (ONI) determines the five consecutive 3-month running mean of sea surface temperature (SST) anomalies, in the Niño 3.4 region (5°S-5°N, 170°W-120°W). El Niño is a phenomenon in the equatorial Pacific Ocean characterized by a value of greater than 0.5°C for ONI. La Niña is a phenomenon in the equatorial Pacific Ocean characterized by a value of less than-0.5°C for ONI. The lingering of either of these two phenomena could induce severe droughts, while either of them following the other could cause massive floods. In both cases, deaths and substantial pecuniary loss is unavoidable, making their forecast of great significance. This study takes over the challenge of forecasting these two phenomena with one year lead time which has proven difficult in the literature. This research's contribution is restructuring ENSO events' predictors, including SST, sea level pressure, surface wind speed, and wind stress in a spatially and temporally meaningful way and designing a convolutional neural network (CNN) that takes advantage of this structure to forecast ENSO events of different types (i.e. Central Pacific and Eastern Pacific) in the next year. Not only a high precision in forecasts was achieved, but also it was shown that the proposed model has the potential to achieve higher recalls if a larger number of samples from the positive class would become available.

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Section: Related Workmentioning

confidence: 99%

Forecasting El Niño and La Niña Using Spatially and Temporally Structured Predictors and a Convolutional Neural Network

Hashemi

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Additionally, the LSTM is prone to overfitting 55 , 56 , hence needing dropout regularization and early call-back mechanisms to reduce overfitting effects. According to Guo et al 57 , the LSTM output is generally computed through function: where h t is the output, o t is the output gate, is the Hadamard product, and C t is the cell status value at time t .…”

Section: Methodsmentioning

confidence: 99%

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam

Huang

Birima

et al. 2022

Sci Rep

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High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.

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“…For example, Peng et al [79] investigated that the CEEMDAN + ConvGRU method can accurately predict the intensity of the South Asian high (SAH) and achieved better stability than the traditional machine learning method. The ensemble empirical mode decomposition (EEMD) combined with CNN + LSTM method proposed by [90] also can predict the El Niño index more accurately and stably.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Peng

Cao

Liu

et al. 2021

Entropy

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The variation of polar vortex intensity is a significant factor affecting the atmospheric conditions and weather in the Northern Hemisphere (NH) and even the world. However, previous studies on the prediction of polar vortex intensity are insufficient. This paper establishes a deep learning (DL) model for multi-day and long-time intensity prediction of the polar vortex. Focusing on the winter period with the strongest polar vortex intensity, geopotential height (GPH) data of NCEP from 1948 to 2020 at 50 hPa are used to construct the dataset of polar vortex anomaly distribution images and polar vortex intensity time series. Then, we propose a new convolution neural network with long short-term memory based on Gaussian smoothing (GSCNN-LSTM) model which can not only accurately predict the variation characteristics of polar vortex intensity from day to day, but also can produce a skillful forecast for lead times of up to 20 days. Moreover, the innovative GSCNN-LSTM model has better stability and skillful correlation prediction than the traditional and some advanced spatiotemporal sequence prediction models. The accuracy of the model suggests important implications that DL methods have good applicability in forecasting the nonlinear system and vortex spatial–temporal characteristics variation in the atmosphere.

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

Cited by 32 publications

References 50 publications

Forecasting El Niño and La Niña Using Spatially and Temporally Structured Predictors and a Convolutional Neural Network

Forecasting El Niño and La Niña Using Spatially and Temporally Structured Predictors and a Convolutional Neural Network

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

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