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Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

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posted on 2024-07-26, 14:01 authored by Fatemeh MekanikFatemeh Mekanik, Monzur ImteazMonzur Imteaz, Shirley Gato-TrinidadShirley Gato-Trinidad, A. Elmahdi
In this study, the application of Artificial Neural Networks (ANN) and Multiple Regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d).The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalization ability for east Victoria with correlation coefficients of -0.99~ -0.90 compared to ANN with correlation coefficients of 0.42~0.93; ANN models also showed better generalization ability for central and west Victoria with correlation coefficients of 0.68~0.85 and 0.58~0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r=0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

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ISSN

0022-1694

Journal title

Journal of Hydrology

Volume

503

Issue

4

Pagination

10 pp

Publisher

Elsevier

Copyright statement

Copyright © 2013 Elsevier B.V. The accepted manuscript of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version has been published in Journal of Hydrology, 503, 2013, http://doi.org/10.1016/j.jhydrol.2013.08.035.

Notes

Non-significant models from this study are available at: http://hdl.handle.net/1959.3/355557

Language

eng

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