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Monthly Rainfall Forecasting Using Temperature and Climate Indices through a Hybrid Method in Queensland, Australia

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  • 1 a Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Victoria, Australia
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Abstract

This paper presents applications of wavelet artificial neural networks (WANN) to forecast rainfalls 1, 3, 6, and 12 months in advance using lagged monthly rainfall, maximum, minimum temperatures, Southern Oscillation index (SOI), interdecadal Pacific oscillation (IPO), and Niño-3.4 as predictors. Eight input datasets composed of different combinations of predictive variables were used for 10 candidate climate stations in Queensland, Australia. Datasets were split as 1908–99 for the training of the model and 2000–16 for the verification of the model. Also, the conventional artificial neural network (ANN) model was developed with the same input datasets to compare with WANN results. Moreover, the skillfulness of the WANN was investigated with the current climate prediction system used by the Australian Bureau of Meteorology (BOM), the Australian Community Climate Earth-System Simulator–Seasonal (ACCESS-S), as well as climatology forecasts. The comparisons showed that the WANN achieved the lowest errors for 3-month lagged prediction with an average root-mean-square error (RMSE) of 38.6 mm. In contrast, for the same lag period, the average RMSEs from ANN, ACCESS-S, and climatology predictions were 72.2, 102.7, and 72.2 mm, respectively. It is also found that the ANN underestimates the peak values with an average value of 49%, 47%, 52%, and 53% at 1, 3, 6, and 12 months lead times, correspondingly. However, the corresponding peak values underestimation through the WANN were 0%, 1%, 22%, and 39%, respectively. This research provides promising insights into using hybrid methods for predicting rainfall a few months in advance, which is extremely beneficial for Australia’s agricultural industries.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0169.1.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Monzur Imteaz, mimteaz@swin.edu.au

Abstract

This paper presents applications of wavelet artificial neural networks (WANN) to forecast rainfalls 1, 3, 6, and 12 months in advance using lagged monthly rainfall, maximum, minimum temperatures, Southern Oscillation index (SOI), interdecadal Pacific oscillation (IPO), and Niño-3.4 as predictors. Eight input datasets composed of different combinations of predictive variables were used for 10 candidate climate stations in Queensland, Australia. Datasets were split as 1908–99 for the training of the model and 2000–16 for the verification of the model. Also, the conventional artificial neural network (ANN) model was developed with the same input datasets to compare with WANN results. Moreover, the skillfulness of the WANN was investigated with the current climate prediction system used by the Australian Bureau of Meteorology (BOM), the Australian Community Climate Earth-System Simulator–Seasonal (ACCESS-S), as well as climatology forecasts. The comparisons showed that the WANN achieved the lowest errors for 3-month lagged prediction with an average root-mean-square error (RMSE) of 38.6 mm. In contrast, for the same lag period, the average RMSEs from ANN, ACCESS-S, and climatology predictions were 72.2, 102.7, and 72.2 mm, respectively. It is also found that the ANN underestimates the peak values with an average value of 49%, 47%, 52%, and 53% at 1, 3, 6, and 12 months lead times, correspondingly. However, the corresponding peak values underestimation through the WANN were 0%, 1%, 22%, and 39%, respectively. This research provides promising insights into using hybrid methods for predicting rainfall a few months in advance, which is extremely beneficial for Australia’s agricultural industries.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0169.1.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Monzur Imteaz, mimteaz@swin.edu.au

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