Artificial neural network modelling technique in predicting Western Australian seasonal rainfall Online publication date: Mon, 01-Feb-2021
by Iqbal Hossain; H.M. Rasel; Fatemeh Mekanik; Monzur Alam Imteaz
International Journal of Water (IJW), Vol. 14, No. 1, 2020
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.
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