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Development of Artificial Intelligence Based Regional Flood Frequency Analysis Technique DEVELOPMENT OF ARTIFICIAL INTELLIGENCE BASED REGIONAL FLOOD FREQUENCY ANALYSIS TECHNIQUE Kashif Aziz, BScEng, MEng Student ID 16658598 A thesis submitted for fulfilment for the degree of Doctor of Philosophy in Civil Engineering Supervisory Panel: Assoc Prof Ataur Rahman Assoc Prof Gu Fang Assoc Prof Surendra Shrestha School of Computing, Engineering and Mathematics University of Western Sydney, Australia December 2014 Artificial Intelligence Based RFFA Aziz ABSTRACT 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 University of Western Sydney II Artificial Intelligence Based RFFA Aziz 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. University of Western Sydney III Artificial Intelligence Based RFFA Aziz STATEMENT OF AUTHENTICTY I certify that all materials presented in this thesis are of my own contribution, and that any work adopted from other sources is duly cited and referenced as such. This thesis contains no material that has been submitted for any award or degree in other university or institution. Kashif Aziz University of Western Sydney IV Artificial Intelligence Based RFFA Aziz ACKNOWLEDGMENTS I would like to express my heartfelt gratitude to Associate Professor Ataur Rahman, who is not only a mentor of mine but a role model as well. This work would have not been possible without his support, encouragement and most importantly the patience during the completion of this work. I am also grateful to Associate Professor Gu Fang and Associate Professor Surendra Shrestha for their valuable advice, support and constructive feedback towards the completion of this research. I could not be prouder of my academic roots and hope that I can in turn pass on the research values and the dreams that my supervisors have given to me. I would not have contemplated this road if not for my parents, Mr. and Mrs. Choudhry Abdul Aziz (late), who instilled within me a love of knowledge and a spirit of struggle to achieve the goal, all of which finds a place in this thesis. To my parents, thank you. I sincerely acknowledge and appreciate the support and patience of my wife Rabia Rehman during this study by looking after myself and our kids. I am also thankful to my family and friends in Australia and overseas for their prayers and encouragement. To the staff and fellow students at University of Western Sydney’s School of Computing Engineering and Mathematics, I am grateful for your help, encouragement and the company I have enjoyed during my candidature. Thank you for welcoming me as a friend and for your moral support. I would like to acknowledge the technical and financial support of all the related Government agencies for providing the resources towards the completion of this research. University of Western Sydney V Artificial Intelligence Based RFFA Aziz Publications made (UNTIL June 2015) from this study Aziz. K., Rahman, A., Fang, G., Shrestha, S. (2014). Application of Artificial Neural Networks in Regional Flood Frequency Analysis: A Case Study for Australia, Stochastic Environment Research & Risk Assessment, 28, 3, 541-554. Aziz, K., Rai, S., Rahman, A. (2014). Design flood estimation in ungauged catchments using genetic algorithm based artificial neural network (GAANN) technique for Australia, Natural Hazards, 77, 2, 805-821. Aziz, K., Rahman, A., Shamseldin, A.Y., Shoaib, M. (2013). Co-Active Neuro Fuzzy Inference System for Regional Flood Estimation in Australia, Journal of Hydrology and Environment Research, 1, 1, 11-20. Aziz, K., Sohail, R., Rahman, A. (2014). Application of Artificial Neural Networks and Genetic Algorithm for Regional Flood Estimation in Eastern Australia, 35th Hydrology and Water Resources Symposium, Perth, Engineers Australia, 24-27 Feb, 2014. Aziz, K., Rahman, A., Shamseldin, A., Shoaib, M. (2013). Regional flood estimation in Australia: Application of gene expression programming and artificial neural network techniques, 20th International Congress on Modelling and Simulation, 1 to 6 December, 2013, Adelaide, Australia, 2283-2289. Aziz, K., Rahman, A., Fang, G. Shrestha, S. (2012). Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy Inference System for Regional Flood Estimation in Australia, Hydrology and Water Resources Symposium, Engineers Australia, 19-22 Nov 2012, Sydney, Australia. Aziz, K., Rahman, A., Shrestha, S., Fang, G. (2011). Derivation of optimum regions for ANN based RFFA in Australia, 34th IAHR World Congress, 26 June – 1 July 2011, Brisbane, 17- 24. Aziz, K., Rahman, A., Fang, G. and Shrestha, S. (2011). Application of Artificial Neural Networks in Regional Flood Estimation in Australia: Formation of Regions Based on Catchment Attributes, The Thirteenth International Conference on Civil, Structural and Environmental Engineering Computing and CSC2011: The Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Chania, University of Western Sydney VI Artificial Intelligence Based RFFA Aziz Crete, Greece, 6-9 September, 2011, 13 pp. Aziz, K., Rahman, A., Fang, G., Haddad, K. and Shrestha, S. (2010). Design flood estimation for ungauged catchments: Application
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