**8. Conclusion**

Other studies, including by Bagirov et al. [40] predicted monthly rainfall for 8 sites in Victoria with 5 input meteorological variables, using data over the period 1889–2014. Montazerolghaem et al. [41] made monthly rainfall forecasts from 136 weather stations in south-eastern Australia in the states of Victoria, NSW, South Australia and southern Queensland with 1 month lead time. Using large scale climate indices, He et al. [42] made forecasts of monthly rainfall at 255 stations across Australia with training data between 1959 and 1889 test data between 1999 and

ANNs have been applied to rainfall forecasting in other parts of the world [25, 26, 43], with **Table 6** showing a survey of 36 articles published between 2014 and 2017 where ANNs have been used to forecast monthly or seasonal rainfall. The majority of these investigations were

ANNs have also been applied to rainfall forecasting at both shorter forecast periods, such as hourly [44] and daily [45] as well as longer forecast periods including annual [46]. ANNs have also been used to forecasting other climatic and meteorological variables including temperatures [47–50] and wind speed [51, 52]. Other machine learning methods have also been applied to rainfall forecasting; including support vector machines (SVM) [53] and adaptive

The survey results detailed in **Table 6** indicate that there have been very few studies of ANNs and their application to rainfall forecasting in North America and Europe. These are regions that are likely to have the longest rainfall and temperature series and therefore are potentially

**Country Region Forecast period Reference**

Australia Queensland and Western Australia Monthly [20] Australia Queensland Monthly [21] Australia Victoria Monthly [40] Australia Bowen Basin, Queensland Monthly [18] Australia Western Australia Monthly [19] Australia south-eastern and eastern Australia Monthly [41] Australia Miles Murray Darling Basin, Monthly [14] Australia Brisbane River catchment, Queensland Monthly [15] Australia Continental Australia Monthly [42] Australia Bowen Basin, Queensland Monthly [16] Australia Queensland Monthly [13]

China Aksu-Tarim River basin Seasonal [57] China Liaoyuan city Monthly [58] China Monthly [59]

2008 with large-scale climate indices as inputs.

neuro-fuzzy inference systems (ANFIS) [54–56].

ideally suited to such studies.

**AUSTRALIA**

**ASIA**

from Asia, particularly India and China.

48 Engineering and Mathematical Topics in Rainfall

While general circulation models are used by meteorological agencies around the world for rainfall forecasting, they do not generally perform well at forecasting medium-term rainfall, despite substantial efforts to enhance performance over many years [36, 81–83]. These are the same models used by the Intergovernmental Panel on Climate Change (IPCC) to forecast climate change over decades. Though recent studies suggest ANNs have considerable application here, including to evaluate natural versus climate change over millennia, and also to better understand equilibrium climate sensitivity [84].

While machine learning is now a well-established discipline, and artificial neural networks a well understood subcomponent, this technology is only beginning to be applied to rainfall forecasting, so far with most of this effort concentrated in China, India and Australia as shown in **Table 6**. The technology is likely to have application to many more regions, particularly North America and Europe where longer time series of rainfall and temperature is likely to exist.

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Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies…

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[9] Queensland Flood Commission of Enquiry, Final Report 2012. http://www.floodcommission.qld.gov.au/\_\_data/assets/pdf\_file/0007/11698/QFCI-Final-Report-March-2012.pdf

[10] Maurice Blackburn Lawyers. Queensland Floods Class Action. https://www.maurice-

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[12] Abbot J, Marohasy J. The potential benefits of using artificial intelligence for monthly rainfall forecasting for the Bowen Basin, Queensland, Australia. Water Resources Management VII. WIT Transactions on Ecology and the Environment. 2013;**171**:287-297

[13] Abbot J, Marohasy J. Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research.

[14] Abbot J, Marohasy J. Forecasting of monthly rainfall in the Murray Darling Basin, Australia: Miles as a case study. WIT Transactions on Ecology and the Environment.

[15] Abbot J, Marohasy J. Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: A case study of the Brisbane catchment, Queensland,

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[19] Abbot J, Marohasy J. Forecasting monthly rainfall in the Western Australian wheat-belt up to 18 months in advance using artificial neural networks. Lecture Notes in Computer

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In this chapter, we demonstrated the application of ANNs for rainfall forecasting for specific locations and also a sub-region within south eastern Queensland, Australia. We demonstrated that the ANN could forecast the extreme flood event of December 2010 that resulted in the catastrophic flooding of Brisbane in January 2011. We show that the skill of the ANN forecast can be improved through single-month optimization of the ANN model, and also by using datasets that extend further back in time. We compare forecasts from the ANN with forecasts from a general circulation model, POAMA, and show that both the deterministic nature of the forecasts, and also their actual skill, indicates that ANNs have much to offer by way of improved monthly rainfall forecasts at least 1 year in advance.
