This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation.

Water shortage issues have been growing concerns in many cities around the world in recent years, especially in Eastern cities of Australia, which is the driest continent on the earth. The aim of this PhD thesis is a development of a model to study the use of waste incineration energy supplemented by alternative energy to power seawater desalination. It is to aid the freshwater supply of a drought stricken city in Eastern Australia. My work contributes to a development of efficient model in a simpler understandable way to reduce efforts required for modelling complex multi domain problems. This research is motivated by the successive severe drought conditions that affected many Australian cities in the past few years, compounded with an additional strain from a fast growing population. While we dump our waste into the Australian landscape, in more densely populated cities in Europe and Asia, the waste is incinerated to obtain thermal energy for various purposes. The waste is used as an energy source while at the same time reducing the amount of space needed for landfill. Seawater desalination has been uccessfully practiced for quite some time particularly in the Middle Eastern countries. To deal with increasing water shortage crisis, many cities around the world have opted or are considering seawater desalination to supplement their freshwater supply. The combination of both - waste incineration and seawater desalination - has rarely been studied. This is a twofold problem that requires modelling the problem of water demand and supply together with waste incineration to find a sustainable solution. This is a complex task. The effort needed for this can be reduced by using a modelling approach that is more efficient than the traditionally used statistical approaches. In this thesis, I present a comprehensive model developed using a dynamic system approach combined with artificial neural networks. It simulates water demand and supply as well as the possible amount of the desalinated water that can be produced using the energy from clean city waste incineration. This is done while taking in various influential factors including population growth and irregular weather patterns. This research comprises a literature review on seawater desalination and waste incineration, the establishment of water demand and supply dynamics of Gold Coast City as my case study and identifying any modelling difficulties that need to be overcome. This is followed by the development of a comprehensive model and its components, model calibration and simulation experiments. It was found that with the energy of waste incineration, up to 60% of the freshwater demand could be fulfilled by seawater desalination in a sustainable way.