**4. Best forecasts from longer datasets**

Our very first paper on forecasting rainfall using ANNs [11] focused on 17 sites, chosen specifically because they had the longest continuous rainfall records for anywhere in Queensland. In that paper, we used a temperature series from the relatively far away destination of Sydney – with the comment that there is no comparable temperature dataset available for any Queensland location. We have since used local temperature series, even if this requires the infilling of missing values through interpolation and/or linear regression [32].

In our early work, we also limited the selection of climate indices to those of 'long duration' – specifically ENSO (SOI and the four Ninos), IPO and DMI. These climate indices were downloaded from the Royal Netherlands Meteorological Institute (KNMI) Climate Explorer.


**Table 2.** Attributes used as input for ANN models.

**Figure 2.** Test results for Bingera showing difference between single-month and all-month NN optimizations [20].

**Table 1.** Skill parameters for monthly rainfall forecasting for Gatton and Harrisville on an annual basis for the test period

July 2004 to August 2011 [15].

38 Engineering and Mathematical Topics in Rainfall

**Figure 3.** Skill scores for monthly forecasts for Victoria Mill (central Queensland), including the comparison between single-month optimization for short duration (reduced-time) and long duration (extended-time) forecasts [20].

There are many more climate indices available, but most are of much shorter published duration, often only beginning in the 1950s. These 'short' duration indices are listed in **Table 2**, specifically the south-eastern Indian Ocean index (SEIO), Western Indian Ocean Index (WIO), The Southern Annular Mode (SAM) and the Quasi-Biannual Oscillation (QBO). Data for these indices were variously sourced from the Bureau, KNMI Climate Explorer, and also the UK Met Office for the recent publication [20] in which we tested the trade-off between more climate indices and shorter series as input.

The skill of an ANN is dependent on finding patterns or relationships in data, and we intuitively thought that input series of less than 100 years would be unlikely to provide adequate information. In a recent study for a region in central Queensland it was apparent that the skill of the forecast depended on the particular month for which the forecast was being made, as shown in **Figure 3**. Nevertheless, it was apparent that of more importance than inputting long-duration series, was single-month optimization. When single month optimization was applied to both long and short duration series, the long duration series gave superior forecasts, as shown in **Figure 3**. More information on the definition of, and methodology used, to calculation the skill score is provided in the technical paper [20] and under the following section: Deterministic versus Probabilistic Forecasts, and Skill Scores.

enables forecasts to be generated for specific locations within the geographical region that do not correspond to sites with rainfall data. It also enables rainfall forecasts to be made corresponding to a defined geographical area within the region, such as a water catchment area. As an example, **Figure 4**, is a specific sub-region of south-eastern Queensland defined by longitudes 151.0°E and 153.5°E, and latitudes 24.4°S and 28.5°S based on rainfall data from 54 sites. The region extends about 300 km southwards along the Queensland Coast from Bundaberg to the Gold Coast, and extended inland from the coast to include the towns of Dalby, Inglewood and Mundubbera, a distance of about 200 km. This sub-region therefore approximates the same area as a single grid forecast area used by POAMA (250 km × 250 km). This example is provided to show that it is possible to develop an isohyet map for such a region derived from the individual series to predict rainfall for a period of intense rainfall, as occurred in December 2010, and also a relatively dry month such as December 2005. The isohyet map was constructed using *Teraplot* software – and the forecast are 12 months in advance

**Figure 4.** Forecast rainfall (mm) for December 2010 for the south-east Queensland region. A: bar chart for individual

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

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using single-month optimization [21].

sites; and B: isohyet map with 50 mm interval spacing [21].
