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Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks

journal contribution
posted on 06.12.2017, 00:00 by John Abbot, Jennifer Marohasy
There have been many theoretical studies of the nature of concurrent relationships between climate indices and rainfall for Queensland, but relatively few of these studies have rigorously tested the lagged relationships (the relationships important for forecasting), particularly within a forecast model. Through the use of artificial neural networks (ANNs) we evaluate the utility of climate indices in terms of their ability to forecast rainfall as a continuous variable. Results using ANNs highlight the value of the Inter-decadal Pacific Oscillation, an index never used in the official seasonal forecasts for Queensland that, until recently, were based on statistical models. Forecasts using the ANN for sites in 3 geographically distinct regions within Queensland are shown to be superior, with lower Root Mean Square Errors (RMSE), Mean Absolute Error (MAE) and Correlation Coefficients (r) compared to forecasts from the Predictive Ocean Atmosphere Model for Australia (POAMA), which is the General Circulation Model currently used to produce the official seasonal rainfall forecasts.

History

Volume

138

Start Page

166

End Page

178

Number of Pages

13

eISSN

1873-2895

ISSN

0169-8095

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

School of Medical and Applied Sciences (2013- ); TBA Research Institute;

Era Eligible

Yes

Journal

Atmospheric research.