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A Comparative Assessment of Models to Predict Monthly Rainfall in Australia

Author

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  • Adil M. Bagirov

    (Federation University Australia)

  • Arshad Mahmood

    (Federation University Australia)

Abstract

Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the k-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy.

Suggested Citation

  • Adil M. Bagirov & Arshad Mahmood, 2018. "A Comparative Assessment of Models to Predict Monthly Rainfall in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1777-1794, March.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:5:d:10.1007_s11269-018-1903-y
    DOI: 10.1007/s11269-018-1903-y
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    References listed on IDEAS

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    1. Garnaut,Ross, 2008. "The Garnaut Climate Change Review," Cambridge Books, Cambridge University Press, number 9780521744447, September.
    2. Jehangir Awan & Deg-Hyo Bae, 2014. "Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1185-1199, March.
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    Cited by:

    1. Meysam Ghamariadyan & Monzur A. Imteaz, 2021. "Prediction of Seasonal Rainfall with One-year Lead Time Using Climate Indices: A Wavelet Neural Network Scheme," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5347-5365, December.
    2. Farhana Islam & Monzur Alam Imteaz, 2022. "A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3649-3672, August.

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