Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators

M. S. Baawain, M. H. Nour, M. G Gamal El-Din

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

ENSO, El Nino southern-oscillation, is known to be the strongest climatic variation on seasonal to inter-annual time scales. It causes severe droughts, floods, fires, and hurricanes leading to economical disasters. Hence, ENSO prediction is crucial to help in preserving world economy. This study explores the use of relatively simple inputs in developing an artificial neural networks (ANN) model for predicting ENSO, identifying the best ENSO indicator for neural networks, and assessing the impact of different time steps on the model efficiency. Two types of ENSO indicators, Southern Oscillation Index (SOI) and NiñoS, were used to develop neural networks models in this study. The use of SOI as an ENSO indicator performed much better compared to NiñoS. ANN model using a monthly time span proved to have a high potential of success. The devised monthly model was a three-layer multi-layer perceptron model trained with error backpropagation algorithm using the logistic activation function for hidden and output layers. The model showed high stability due to the similar R2values when the training and testing data patterns in the Input data sets were swapped.

Original languageEnglish
Title of host publicationProceedings, Annual Conference - Canadian Society for Civil Engineering
Pages858-867
Number of pages10
Volume2003
Publication statusPublished - 2003
EventCanadian Society for Civil Engineering - 31st Annual Conference: 2003 Building our Civilization - Moncton, NB, Canada
Duration: Jun 4 2003Jun 7 2003

Other

OtherCanadian Society for Civil Engineering - 31st Annual Conference: 2003 Building our Civilization
CountryCanada
CityMoncton, NB
Period6/4/036/7/03

Fingerprint

Neural networks
Backpropagation algorithms
Drought
Hurricanes
Multilayer neural networks
Disasters
Logistics
Fires
Chemical activation
Testing

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Baawain, M. S., Nour, M. H., & El-Din, M. G. G. (2003). Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators. In Proceedings, Annual Conference - Canadian Society for Civil Engineering (Vol. 2003, pp. 858-867)

Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators. / Baawain, M. S.; Nour, M. H.; El-Din, M. G Gamal.

Proceedings, Annual Conference - Canadian Society for Civil Engineering. Vol. 2003 2003. p. 858-867.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Baawain, MS, Nour, MH & El-Din, MGG 2003, Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators. in Proceedings, Annual Conference - Canadian Society for Civil Engineering. vol. 2003, pp. 858-867, Canadian Society for Civil Engineering - 31st Annual Conference: 2003 Building our Civilization, Moncton, NB, Canada, 6/4/03.
Baawain MS, Nour MH, El-Din MGG. Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators. In Proceedings, Annual Conference - Canadian Society for Civil Engineering. Vol. 2003. 2003. p. 858-867
Baawain, M. S. ; Nour, M. H. ; El-Din, M. G Gamal. / Applying artificial neural network models for ENSO prediction using SOI and Nino3 as onset indicators. Proceedings, Annual Conference - Canadian Society for Civil Engineering. Vol. 2003 2003. pp. 858-867
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