Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Abbot, John, and Marohasy, Jennifer (2017) Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks. International Journal of Sustainable Development and Planning, 12 (7). pp. 1117-1131.

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Extreme rainfall in Queensland during December 2010 and January 2011 resulted in catastrophic flooding, causing loss of life, extensive property damage and major disruption of economic activity. Official medium-term rainfall forecasts failed to warn of the impending heavy rainfall. Since the flooding, the Australian Bureau of Meteorology has changed its method of forecast from an empirical statistical scheme to the application of a general circulation model (GCM), the Predictive Ocean and Atmospheric Model for Australia (POAMA). Our previous studies demonstrated that more skilful monthly rainfall forecasts can be achieved using artificial neural networks (ANNs). This study extends those previous investigations focussing on the capacity of the forecast methodology to differentiate between extreme rainfall events and more average conditions, up to one year in advance. Sites within two geographical regions of Queensland are examined: (i) coastal Queensland using rainfall observations from Bingera, Plane Creek and Victoria Mill; (ii) a region of south-east Queensland, using rainfall observations from 54 weather stations, extending approximately 300 km northward along the Queensland coast, from the Gold Coast to Bundaberg, and approximately 200 km inland. For both regions, the capacity to differentiate between average conditions and impending extreme rainfall events up to one year in advance is demonstrated.

Item ID: 47745
Item Type: Article (Research - C1)
ISSN: 1743-7601
Keywords: artificial neural network; flood; forecast; rainfall; Queensland
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Funders: B. Macfie Family Foundation
Date Deposited: 14 Mar 2017 04:25
FoR Codes: 37 EARTH SCIENCES > 3701 Atmospheric sciences > 370108 Meteorology @ 100%
SEO Codes: 96 ENVIRONMENT > 9602 Atmosphere and Weather > 960203 Weather @ 100%
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