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Australasian Joint Conference on Artificial Intelligence

AI 2016: AI 2016: Advances in Artificial Intelligence pp 88–100Cite as

Forecasting Monthly Rainfall in the Bowen Basin of Queensland, Australia, Using Neural Networks with Niño Indices

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9992)

Abstract

For three decades there has been a significant global effort to improve El Niño-Southern Oscillation (ENSO) forecasts with the focus on using fully physical ocean-atmospheric coupled general circulation models (GCMs). Despite increasing sophistication of these models and the computational power of the computers that drive them, their predictive skill remains comparable with relatively simple statistical models. In this study, an artificial neural network (ANN) is used to forecast four indices that describe ENSO, namely Niño 1 + 2, 3, 3.4 and 4. The skill of the forecast for Niño 3.4 is compared with forecasts from GCMs and found to be more accurate particularly for forecasts with longer-lead times, and with no evidence of a Spring Predictability Barrier. The forecast values for Niño 1 + 2, 3, 3.4 and 4 were subsequently used as input to an ANN to forecast rainfall for Nebo, a locality in the Bowen Basin, a major coal-mining region of Queensland.

Keywords

  • ENSO
  • Niño
  • Sea surface temperature
  • Artificial neural network
  • General circulation model
  • Rainfall
  • Spring predictability barrier

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Acknowledgements

This work was funded by the B. Macfie Family Foundation.

Author information

Authors and Affiliations

  1. Climate Modelling Laboratory, Noosaville, QLD, Australia

    John Abbot & Jennifer Marohasy

  2. Department of Engineering, University of Tasmania, Hobart, TAS, Australia

    John Abbot

  3. Institute of Public Affairs, Melbourne, VIC, Australia

    John Abbot & Jennifer Marohasy

Authors
  1. John Abbot
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  2. Jennifer Marohasy
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Corresponding author

Correspondence to John Abbot .

Editor information

Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Abbot, J., Marohasy, J. (2016). Forecasting Monthly Rainfall in the Bowen Basin of Queensland, Australia, Using Neural Networks with Niño Indices. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_7

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