A wavelet artificial neural network method for medium‐term rainfall prediction in Queensland (Australia) and the comparisons with conventional methods

This study aims to develop a medium‐term rainfall forecast model to predict monthly rainfall 1, 3, 6, and 12 months in advance using hybrid wavelet‐artificial neural networks (WANN) for Queensland, Australia. To assess the performance of the models, seven input sets comprised of either past rainfall magnitudes, selected climate anomalies, or combinations of rainfall with different climate anomalies as independent variables were defined. The data of the years 1908–1999 and 2000–2016 from 10 weather stations in Queensland were used for training and verification of the models, respectively. The results showed that the WANN enhances the average prediction accuracy in terms of root‐mean‐square‐error (RMSE) with 90, 52, 32, and 15% at 1, 3, 6, and 12 months lead time, respectively compared to of the artificial neural networks (ANN). Also, the current prediction system of the Australian Bureau of Meteorology (BOM), Australian Community Climate Earth‐System Simulator–Seasonal (ACCESS‐S) provided the predictions with the average RMSE values of 91.0, 102.7, and 100.3 mm for 1, 3, and 6 months lead times, respectively. In contrast, the corresponding RMSE values of 8.2, 37.6, and 52.4 mm are obtained through the WANN. Moreover, performance of the WANN method was compared with the autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) models. ARIMA and MLR produced similar performances at same lead times; corresponding average RMSE values of 78.8, 82.72, 80.9, and 79.5 mm were obtained through ARIMA compared to the 75.9, 79.5, 79.0, and 78.9 mm through the MLR. The results of the current study indicate than the performance of the WANN is more accurate than the ANN, ARIMA, MLR, and ACCESS‐S forecasts. Future rainfall prediction is complex due to the presence of different effective climatological factors. It is even more intricate in Australia because of its high grade of temporal and spatial inconsistency. Therefore, it is highly beneficial to predict rainfall a few months in advance to manage unusual climatological events. Hence, the wavelet‐ANN technique is used to obtain better results of forecasting monthly rainfall. The results show that WNN outperforms conventional prediction methods as well as the BOM's current prediction system.