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Multi-Year ENSO Forecasts Using Parallel Convolutional Neural Networks With Heterogeneous Architecture
2021
Frontiers in Marine Science
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To solve this problem, we propose a novel parallel deep convolutional neural network (CNN) with a heterogeneous architecture. ...
The El Niño-Southern Oscillation (ENSO) is one of the main drivers of the interannual climate variability of Earth and can cause a wide range of climate anomalies, so multi year ENSO forecasts are a paramount ...
Prediction of enso beyond spring predictability barrier using deep convolutional lstm networks. IEEE Geosci. Remote Sens. ...
doi:10.3389/fmars.2021.717184
fatcat:v2hvd26jajeorac54kgcetq3sa
ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
2021
Geoscientific Model Development
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On the basis of this coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder and decoder ...
We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models. ...
In ENSO forecasting, such a forecast skill decline is regarded as a persistence barrier and usually occurs in spring (i.e., spring predictability barrier, SPB) (Webster, 1995; Zheng and Zhu, 2010) . ...
doi:10.5194/gmd-14-6977-2021
fatcat:devwpqfjard7xkrtrpwd5kwwqu
Physics captured by data-based methods in El Niño prediction
[article]
2022
arXiv
pre-print
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Recent machine-learning approaches to El Ni\~no prediction, in particular Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. ...
In an attempt to understand this high skill, we here use data from distorted physics simulations with an intermediate complexity El Ni\~no model to determine what aspects of El Ni\~no physics are represented ...
The research described above shows that DLMs are a very promising tool in ENSO prediction that can provide useful skill of El Niño forecasts beyond the predictability barriers. ...
arXiv:2206.03110v1
fatcat:bltaqg3fczekliwbpxegwloeou
International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings]
2020
2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)
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ACKNOWLEDGMENT The authors would like to thank the Department of Census and Department of Irrigation, Sri Lanka for providing the paddy yield and climate data for this study. ...
Kelaniya for their immense support and encouragement they gave throughout the development phase of the data sets. ...
"cnn-trad-fpool3" was contained, two convolutional, one linear low-rank, and one Deep Neural Network (DNN) layer. ...
doi:10.1109/scse49731.2020.9313027
fatcat:gjk5az2mprgvrpallwh6uhvlfi
Eastern Cardiothoracic Surgical Society 55th Annual Meeting
2018
Innovations (Philadelphia): technology and techniques in cardiothoracic and vascular surgery
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While models based on a form of RNN known as the long short-term memory (LSTM) network have performed successfully on tasks such as image annotation, a shortcoming of LSTM networks is that they are composed ...
The raw video data is passed through a "deep" or many-layered convolutional neural network for classifying superpixels, an average pooling layer, a layer that performs the POISE algorithm to merge superpixels ...
doi:10.1097/imi.0000000000000471
fatcat:m7dkilztybhelp2q6upbwkkyle
Knowledge Extracted from Copernicus Satellite Data
2019
Zenodo
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The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts. References 1. ...
During the development of deep learning algorithms, a key activity is to establish a large amount of referenced Earth Observation data. ...
Popularization platform focus on network popularization of investigation knowledge. ...
doi:10.5281/zenodo.3941573
fatcat:zzifwgljifck5bpjnboetsftfu