Title: Artificial neural network modelling technique in predicting Western Australian seasonal rainfall

Authors: Iqbal Hossain; H.M. Rasel; Fatemeh Mekanik; Monzur Alam Imteaz

Addresses: Faculty of Science, Engineering and Technology, Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia ' Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi – 6204, Bangladesh ' Faculty of Science, Engineering and Technology, Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia ' Faculty of Science, Engineering and Technology, Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Abstract: This paper presents the efficiency of non-linear modelling technique in predicting long-term seasonal rainfall of Western Australia. One of the commonly used non-linear modelling approaches, artificial neural network (ANN) was adopted for the construction of the non-linear models. The models were developed considering the past values of El Nino southern oscillation (ENSO) and Indian Ocean Dipole (IOD) as the probable influential variables of rainfall. The ANN models were constructed adopting the algorithm proposed by Lavenberg-Marquardt. The models were developed and tested for three rainfall stations in Western Australia. The models showed good generalisation capability of Western Australian spring rainfalls with Pearson correlations varying from 0.46 to 0.82 during the training phase and 0.55 to 0.96 during the testing phase. The errors and index of agreement of the IOD-ENSO based ANN models were also acceptable to be applied for rainfall forecasting.

Keywords: non-linear modelling; seasonal rainfall; rainfall forecasting; ANN; artificial neural network; ENSO; El Nino southern oscillation; IOD; Indian Ocean Dipole.

DOI: 10.1504/IJW.2020.112711

International Journal of Water, 2020 Vol.14 No.1, pp.14 - 28

Received: 24 Sep 2019
Accepted: 16 Jan 2020
Published online: 01 Feb 2021 *

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