- Title
- A comparative assessment of models to predict monthly rainfall in Australia
- Creator
- Bagirov, Adil; Mahmood, Arshad
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164383
- Identifier
- vital:13043
- Identifier
-
https://doi.org/10.1007/s11269-018-1903-y
- Identifier
- ISBN:0920-4741
- 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. © 2018, Springer Science+Business Media B.V., part of Springer Nature.
- Publisher
- Springer Netherlands
- Relation
- Water Resources Management Vol. 32, no. 5 (2018), p. 1777-1794; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2018, Springer Science+Business Media B.V., part of Springer Nature.
- Rights
- This metadata is freely available under a CCO license
- Subject
- MD Multidisciplinary; Prediction models; Prediction performance; Rainfall prediction; Regression analysis
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