Flood is one of the worst natural disasters, which brings disruptions to services and damages to infrastructure, crops and properties and sometimes causes loss of human lives. In Australia, the average annual flood damage is worth over $377 million, and infrastructure requiring design flood estimate is over $1 billion per annum. The 2010-11 devastating flood in Queensland alone caused flood damage over $5 billion. Design flood estimation is required in numerous engineering applications e.g., design of bridge, culvert, weir, spill way, detention basin, flood protection levees, highways, floodplain modelling, flood insurance studies and flood damage assessment tasks. For design flood estimation, the most direct method is flood frequency analysis, which requires long period of recorded streamflow data at the site of interest. This is not a feasible option at many locations due to absence or limitation of streamflow records. For these ungauged or poorly gauged catchments, regional flood frequency analysis (RFFA) is adopted. The use of RFFA enables the transfer of flood characteristics information from gauged to ungauged catchments. RFFA essentially consists of two principal steps: (i) formation of regions; and (ii) development of prediction equations. For developing the regional flood prediction equations, the commonly used techniques include the rational method, index flood method and quantile regression technique. These techniques adopt a linear method of transforming inputs to outputs. Since hydrologic systems are non-linear, RFFA techniques based on non-linear method can be a better alternative to linear methods. Among the non-linear methods, artificial intelligence based techniques have been widely adopted to various water resources engineering problems. However, their application to RFFA is quite limited. Hence, this research focuses on the development of artificial intelligence based RFFA methods for Australia. The non-linear techniques considered in this thesis include artificial neural network (ANN), genetic algorithm based artificial neural network (GAANN), gene-expression programing (GEP) and co-active neuro fuzzy inference system (CANFIS). This study uses data from 452 small to medium sized catchments from eastern Australia. In the development/training of the artificial intelligence based RFFA models, the selected 452 catchments are divided into two parts randomly: (i) training data set consisting of 362 catchments; and (ii) validation data set consisting of 90 catchments. It has been found that a RFFA model with two predictor variables i.e., catchment area and design rainfall intensity provides more accurate flood quantile estimates than other models with a greater number of predictor variables. The results show that when the data from all the eastern Australian states are combined to form one region, the resulting ANN based RFFA model performs better as compared with other candidate regions such as regions based on state boundaries, geographical and climatic boundaries and the regions formed in the catchment characteristics data space. In the training of the four artificial intelligence based RFFA models, no model performs the best for all the six average recurrence intervals over all the adopted statistical criteria. Overall, the ANN based RFFA model performs better than the three other models in the training/calibration. In this research, it also has been found that non-linear artificial intelligence based RFFA techniques can be applied successfully to eastern Australian catchments. Among the four artificial intelligence based models considered in this study, the ANN based RFFA model has demonstrated best performance based on independent split-sample validation, followed by the GAANN based RFFA model. The ANN based RFFA model has been found to outperform the ordinary least squares based RFFA model. Based on independent validation, the median relative error values for the ANN based RFFA model are found to be in the range of 35% to 44% for eastern Australia, which is comparable to the generalised least squares regression and region-of-influence based RFFA approach. The ANN based RFFA model exhibits no noticeable spatial trend in the relative error values. Furthermore, the relative error values of the ANN based RFFA model are found to be independent of catchment area. The findings of this research would help to recommend the most appropriate RFFA techniques in the 4th edition of Australian Rainfall and Runoff, which is due to be published in 2015.
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