Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/235106

Title

Forecasting Victorian spring rainfall using ENSO and IOD: a comparison of linear multiple regression and nonlinear ANN

Author(s)

Mekanik, F.;  Imteaz, M. A.

Abstract

El Nino southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) have enormous effects on the precipitations around the world. Australian rainfall is also affected by these key modes of complex climate variables. Many studies have tried to establish the relationships of these large-scale climate indices among the rainfalls of different parts of Australia, particularly Western Australia, New South Wales, Queensland and Victoria. Unlike the other regions, no clear relationship can be found between each individual largescale climate mode and Victorian rainfall. Past studies considering southeast Australian rainfall predictability could achieve a maximum of 30% correlation. This study looks into the lagged-time relationships of these modes on Victorian spring rainfall. On the other hand, few attempts have been made to establish the combined effect of these indices on rainfall in order to develop a better understanding and forecasting system. Thus, the aim of this research was to investigate the combined lagged relationship of ENSO and IOD with Victorian spring rainfall using multiple regression as a linear method compared to Artificial Neural Networks (ANN) as a nonlinear method. This study found that predicting spring rainfall using combined lagged ENSO-DMI indices with ANN can achieve 96.96% correlation as compared to multiple regression with only 66.15% correlation. This method can be used for other parts of the world where a relationship exists between rainfall and large scale climate modes which could not be established by linear methods.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Engineering and Industrial Sciences

Source

Proceedings of the 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE 2012), Jakarta, Indonesia, 14-15 August 2012, pp. 86-89

Publication year

2012

Keyword(s)

ANN;  ENSO;  Forecasting;  IOD;  Multiple regression;  Rainfall

Publisher

IEEE

ISBN

9781467314602

Publisher URL

http://ieeexplore.ieee.org/

Copyright

Copyright © 2012 IEEE.

Peer reviewed