Prediction of Seasonal Rainfall with One-year Lead Time Using Climate Indices: A Wavelet Neural Network Scheme

摘要

This paper presents the development of the Wavelet Artificial Neural Networks (WANN) model to forecast seasonal rainfall in Queensland, Australia, using the Inter-decadal Pacific Oscillation (IPO), Southern Oscillation Index (SOI), and Nino3.4 climate indices as predictors. Eight input sets with different combinations of predictive variables from 1908 to 2016 were considered to develop forecast models for ten selected rainfall stations in Queensland, Australia. The outcomes of WANN modeling are compared with Artificial Neural Networks (ANN). Moreover, the skillfulness of the WANN in comparison to the current climate prediction system used by the Australian Community Climate Earth-System Simulator-Seasonal (ACCESS-S) and climatology forecasts are investigated. Besides, the WANN predictions are compared with two other conventional approaches like autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for further investigations. The comparisons indicated that the WANN achieves the lower average root mean square error (RMSE) in all the stations with 112.2mm compared to ANN with 178.9mm, ACCESS-S with 281.8mm, climatology prediction with 279.7mm, MLR with 195.1mm, and ARIMA with 187.7mm. The WANN seasonal rainfall forecasts are more accurate than the ANN, ACCESS-S, Climatology, MLR, and ARIMA by 37, 60, 53, 42, and 40, respectively. It was also found that the ACCESS-S underestimates the extreme seasonal rainfall during the testing period up to 80, while it is limited to 21 for the WANN among the selected stations. The results show that the WANN model outperforms the MLR, ARIMA, climatology, ACCESS-S, and ANN forecasts in all the selected stations.