El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks

2019 ◽  
Vol 24 (4) ◽  
pp. 445-451 ◽  
Author(s):  
Clifford Broni-Bedaiko ◽  
Ferdinand Apietu Katsriku ◽  
Tatsuo Unemi ◽  
Masayasu Atsumi ◽  
Jamal-Deen Abdulai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Complex Networks ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Networks Analysis

scholarly journals Streamflow prediction of the Yangtze River base on deep learning neural networks: Impact of the El Niño–Southern Oscillation

2021 ◽  
Author(s):  
Si Ha ◽  
Darong Liu ◽  
Lin Mu
Keyword(s):  
Neural Networks ◽  
El Niño ◽  
Yangtze River ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
The Yangtze River ◽  
Monthly Streamflow ◽  
Extreme Flood

Abstract Accurate long-term streamflow and flood forecasting has always been an important research direction in hydrology research. Nowadays, with climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about one month in advance, which is too short for decision making. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2016 to predict the monthly streamflow of the Yangtze River in two extreme flood years by using deep neural networks. In this paper, three deep neural network frameworks are used: Stacked LSTM, Conv LSTM Encoder-Decoder LSTM and Conv LSTM Encoder-Decoder GRU. Experiments have shown that the months of flood occurrence and peak flows predicted by these four models become more accurate after the introduction of ENSO. And the best results were obtained on the Convolutional LSTM + Encoder Decoder Gate Recurrent Unit model.

VARIABILIDAD CLIMÁTICA, CAMBIO CLIMÁTICO Y PESQUERÍAS EN EL ECUADOR.

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Johnny Chavarría Viteri ◽  
Dennis Tomalá Solano
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Pacific Decadal Oscillation ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Current Action ◽  
Palabras Clave

La variabilidad climática es la norma que ha modulado la vida en el planeta. Este trabajo demuestra que las pesquerías y acuicultura costera ecuatorianas no son la excepción, puesto que tales actividades están fuertemente influenciadas por la variabilidad ENSO (El Niño-Oscilación del Sur) y PDO (Oscilación Decadal del Pacífico), planteándose que la señal del cambio climático debe contribuir a esta influencia. Se destaca también que, en el análisis de los efectos de la variabilidad climática sobre los recursos pesqueros, el esfuerzo extractivo también debe ser considerado. Por su parte, la acción actual de la PDO está afectando la señal del cambio climático, encontrándose actualmente en fases opuestas. Se espera que estas señales entren en fase a finales de esta década, y principalmente durante la década de los 20 y consecuentemente se evidencien con mayor fuerza los efectos del Cambio Climático. Palabras Clave: Variabilidad Climática, Cambio Climático, ENSO, PDO, Pesquerías, Ecuador. ABSTRACT Climate variability is the standard that has modulated life in the planet. This work shows that the Ecuadorian  fisheries and aquaculture are not the exception, since such activities are strongly influenced by ENSO variability (El Niño - Southern Oscillation) and PDO (Pacific Decadal Oscillation), considering that the signal of climate change should contribute to this influence. It also emphasizes that in the analysis of the effects of climate variability on the fishing resources, the extractive effort must also be considered. For its part, the current action of the PDO is affecting the signal of climate change, now found on opposite phases. It is hoped that these signals come into phase at the end of this decade, and especially during the decade of the 20’s and more strongly evidencing the effects of climate change. Keywords: Climate variability, climate change, ENSO (El Niño - Southern Oscillation) and PDO  (Pacific Decadal Oscillation); fisheries, Ecuador. Recibido: mayo, 2012Aprobado: agosto, 2012

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