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Disasters caused by floods are a major cause of losses of properties and lives. The unpredictability in weather conditions due to changing weather patterns do not only lead to flooding but also contribute to water resource management problems. Rapid development in many tropical countries, like in Malaysia, has resulted in the loss of natural floodplains leading to an increase in flooding and water shortage. Sufficient advanced flood warning system that can save lives and properties can be developed using accurate river model. The work reported in this thesis has made significant contributions in the prediction of river flow rate based on rainfall rate in the catchment area using Artificial Neural Network (ANN). The proposed approach models the non-linear process of the rainfall-runoff in a wide variety of catchment area conditions. This study demonstrates significant improvement in the accuracy and reliability of water resource management by using ANN modelling to predict river flow rate. It also shows ANN as a fast and adaptable approach that is suitable for river flow rate modelling that does not need detailed geographical information of the catchment area. Its attractiveness is in its ability to adapt to changing conditions and therefore does not become outdated like conventional hydrology models. The research shows that river flow rate is a better parameter to be used for an early flood warning system as it is more sensitive to rainfall rate compared to the river level which is used in conventional flood warning systems. The study has also shown that ANN with a feed-backward network with one hidden layer provides the best results and it is able to produce river flow rate prediction up to 132 hours with root mean square error of 0.02 m3/s . This is a significant contribution as the flood warning system currently used in Malaysia can only predict flooding within 8 to 24 hours. The work in this thesis can assist the authorities to manage water from dams thereby effectively managing floods and ensuring sufficient water for domestic and agricultural use. The findings of this research has already been presented to the Malaysian government agency responsible for managing waterways and dams.

This thesis provides a new space-time statistical rain model and a novel space-time interpolation approach for planning and dimensioning wide area high frequency satellite communication networks. Key characteristics of rainfall rate fields are modelled. These include detailed description of: (i) the first order statistical distribution, (ii) the spatial and temporal correlation functions of rainfall rate and, and (iii) the probability of rain/no-rain. With a focus on their relevance to satellite and terrestrial microwave network design, the key contribution of this study is the assessment of the impact of varying spatial and temporal integration lengths on these quantities. The issue of how these key characteristics of rainfall rate field change with different area sizes are analysed in this thesis and it is novel. A simple but accurate interpolation approach of the key characteristic parameters is presented in this thesis. The novelty of the proposed technique is that it does not rely directly on the radar/raingauge derived rainfall rate data like traditional models do but rather on fitted coefficients and computed rain characteristics. This thesis proposes rain parameter contour maps and databases covering the whole of Western Europe from which users can conveniently obtain the key rain characteristic parameters at any location within the studied area. More speculatively, the space-time interpolation approach can extrapolate to rain parameters at space-time resolutions shorter than those in the NIMROD databases. The results have been validated by comparing them with those from ITU Rec model and measurements by NIMROD rain radar. In addition, a Graphical User Interface (GUI) software has been provided that allows users to interact with the proposed model. The user can easily obtain the information of the key rain characteristics at different space-time scales by simply inputting the longitude, latitude, space resolution and time resolution of the location of interest. The detailed results are then automatically calculated and displayed by the software significantly facilitating rain rate study.