**Author details**

**Figure 10.** Rainfall during winter season across solar cycle.

28 Engineering and Mathematical Topics in Rainfall

**Figure 11.** Rainfall during autumn season across solar cycle.

**Figure 12.** Comparison chart.

Shajimon K John

Address all correspondence to: shajimonkalayil@gmail.com

Saintgits College of Engineering, Kottayam, Kerala, India

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**Chapter 3**

**Provisional chapter**

**Forecasting of Medium-term Rainfall Using Artificial**

**Neural Networks: Case Studies from Eastern Australia**

**Forecasting of Medium-term Rainfall Using Artificial** 

John Abbot and Jennifer Marohasy

John Abbot and Jennifer Marohasy

http://dx.doi.org/10.5772/intechopen.72619

**Abstract**

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

**Neural Networks: Case Studies from Eastern Australia**

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in southeastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia.

**Keywords:** rainfall, forecast, monthly, neural network, Australia

DOI: 10.5772/intechopen.72619

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Until relatively recently, simple statistical models were used by meteorological agencies around the world to forecast seasonal and monthly rainfall. Typically, these models use relationships between large scale climate indices, such as the Southern Oscillation Index, and rainfall at some future time, generally utilizing a small number of input variables, perhaps only one or two. For example, until May 2013 the Australian Bureau of Meteorology (BOM) generated seasonal rainfall forecasts based on a statistical scheme using an El Niño Southern Oscillation (ENSO) index as a primary predictor in a relatively simple statistical model [1, 2]. These traditional statistical models are limited in the number of input variables that can be effectively combined, while advances in machine learning has now significantly expanded

**Provisional chapter**
