- Title
- Prediction of monthly rainfall in Victoria, Australia : Clusterwise linear regression approach
- Creator
- Bagirov, Adil; Mahmood, Arshad; Barton, Andrew
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/155367
- Identifier
- vital:11231
- Identifier
-
https://doi.org/10.1016/j.atmosres.2017.01.003
- Identifier
- ISSN:0169-8095
- Abstract
- This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889–2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations. © 2017 Elsevier B.V.
- Publisher
- Elsevier Ltd
- Relation
- Atmospheric Research Vol. 188, no. (2017), p. 20-29; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2017 Elsevier B.V. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0401 Atmospheric Sciences; 0299 Other Physical Sciences; Cluster analysis; Clusterwise linear regression; Prediction models; Rainfall prediction; Regression analysis
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