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Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks

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AI 2016: Advances in Artificial Intelligence (AI 2016)

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Abstract

Accurate medium-term rainfall forecasts are a significant constraint to dry land cropping. In Australia, official monthly forecasts for the Western Australian wheat-belt are currently based on output from the Bureau of Meteorology’s general circulation model, the Predictive Ocean Atmosphere Model for Australia (POAMA). These forecasts are provided in a two-category format (above or below median rainfall) up to three months in advance for large grid areas, and are not considered reliable. An alternative approach is presented here for the three locations of Narrogin, Merredin and Southern Cross using artificial neural networks (ANNs) to forecast monthly rainfall up to 18 months in advance. Skilful monthly rainfall forecasts can be achieved at all lead times measured in terms of root mean square error (RMSE) and mean absolute error (MAE). This approach is of practical benefit to wheat growers in this region, with potential application to other locations with long historical temperature and rainfall records.

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Acknowledgements

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

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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

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  1. John Abbot
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Correspondence to John Abbot .

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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 Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_6

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