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University of Alberta River Ice Breakup Forecasting Using Artificial Neural

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University of Alberta River Ice Breakup Forecasting Using Artificial Neural University of Alberta River ice breakup forecasting using artificial neural networks and fuzzy logic systems by Liming Zhao A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Water Resources Engineering Department of Civil and Environmental Engineering ©Liming Zhao Fall 2012 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. Abstract Due to the complexity of breakup ice jam processes deterministic modelling cannot yet forecast every aspect of the timing and severity of possible consequent flooding, especially when some lead-time is needed. In most northern regions, the sparse network and short record of data have impeded the successful development of empirical and statistical models. In this study, a multi-layer modeling approach was investigated for forecasting breakup ice jam flooding using the two soft computing techniques: artificial neural networks and fuzzy logic systems. The Town of Hay River in NWT, Canada was chosen as the case study site, where the breakup ice jam flooding is an annual threat. This thesis first presents the development of index variables as potential predictors to breakup severity and timing. For the case study site, it was found that water level at the onset of freeze-up and accumulated degree-days of freezing during the winter could be potential predictors for breakup severity. The indicator variable of the timing of the onset of breakup was found to be completely nonlinear with respect to any of the index variables. Then the feed-forward artificial neural network (ANN) modeling technique was assessed for its applicability in forecasting of onset of breakup. Detailed results of the ANN model calibration and validation are presented and discussed. It was found from the calibration results, that the ANN model has greater potential for successfully forecasting the onset of river ice breakup (i.e. the first transverse cracking of the ice cover) compared to the conventional multiple linear regression technique. However, rigorous validation also indicated that the accuracy of such ANN models can be optimistically overestimated by looking only at the calibration results. Finally, the applicability of a Mamdani-type fuzzy logic system to forecast the peak snowmelt runoff during breakup for a long lead-time of ~3 to 4 weeks prior to breakup was assessed, and was found to be a good predictor of breakup flood severity at the Town of Hay River. In particular, it was found that the fuzzy logic model could predict most of the high flow, the exception being those that were triggered by short intense rainfall events during the breakup period (a factor that cannot be included in a long lead-time forecast). This study contributes new knowledge and techniques, advancing the breakup ice jam flood forecasting capabilities for the northern communities. The two most common soft computing techniques (e.g. ANN and fuzzy logic system) were studied comprehensively for their potential in river ice breakup forecasting and demonstrated step by step at the case study site. A hydrometeorological data base for the Town of Hay River was also established for the further research. Acknowledgements The author would like to first thank his supervisor, Dr. Faye Hicks for her tremendous unselfish help and guidance in this study. Dr. Hicks’ comprehensive professional knowledge, great scientific insight, persistent passion for research has a great influence and is a lifetime example to the author. It is also much appreciated for the opportunities of take part in the field work attending the conferences supported by Dr. Hicks. The author is also grateful to his co- supervisor Dr. Aminah Robinson Fayek for her advice and support over the course of this study. Dr. Robinson Fayek’s keen interest and knowledge in the fuzzy logic technique has great helped this study keep going on the right track. Her time is greatly appreciated. The author also would like to thank the rest of the defense committee, Dr. Mark Loewen, Dr. Witold Pedrycz, Dr. Karen Dow Ambtman, and in particular the external examiner Dr. Leonard Lye from Memorial University of Newfoundland, for their time and effort in reviewing this thesis. The author was financially supported by the University of Alberta through the F.S. Chia and QE II scholarships, the R (Larry) Gerard Memorial Graduate Scholarship in Ice Engineering, and various teaching and research assistantships. The author’s study was also funded by NSERC through the Strategic Grants, Discovery Grants and Northern Research Supplement programs. The travel awards to the author by the Canadian Geophysical Union’s Committee on River Ice Processes and the Environment, the University of Alberta’s Graduate Student Association, through the Professional Development Grant and from Alberta Advanced Education and Technology through the Profiling Alberta’s Graduate Students Award are also acknowledged. Many thanks to Aboriginal Affairs and Northern Development Canada (AANDC) for their participation in, and support of, the research with particular thanks to Shawne Kokelj and Meg McCluskie. Sincere thanks to Water Survey of Canada for providing extensive amounts of data in support of this study and in particular thank Randy Wedel, Roger Pilling, Dennis Lazowski and Shayla Pagonis as well as their staff and co-workers. Thanks also to the Town of Hay River for their interest in and extensive support of this research. Thanks also go to the whole river ice group at the University of Alberta: Robyn Andrishak, Maxime Belanger, Michael Brayall, Perry Fedun, Agata Hall, Nadia Kovachis, Chris Krath, Joshua Maxwell, Janelle Morley, Jennifer Nafziger, Yuntong She, David Watson, and in particular Wendy Howard and Brett Howard for their assistance in processing the Hay River historical data. All the helps and friendships make this study memorable at the University of Alberta. Finally, I would give special thanks to my wife, Jianan Cai, for her endless encouragement to me to complete this thesis, to my newborn baby girl, Chenyi Zhao, for giving me much motivation to finish the thesis writing, and to my parents and parents-in-law for their forever love and always standing by me. Table of Contents Chapter 1 Introduction .......................................................................................... 1 1.1 Background .................................................................................................... 2 1.2 Forecasting breakup flood severity ............................................................... 7 1.2.1 Threshold models .................................................................................... 8 1.2.2 Multiple linear regression models ......................................................... 10 1.2.3 Logistic regression models ................................................................... 12 1.2.4 Discriminant function analysis.............................................................. 12 1.2.5 Artificial neural networks (ANNs) ....................................................... 13 1.2.6 Fuzzy logic systems .............................................................................. 15 1.2.7 Discussions ........................................................................................... 16 1.3 Forecasting the timing of breakup ............................................................... 17 1.3.1 Semi-empirical models ......................................................................... 18 1.3.2 Empirical models .................................................................................. 20 1.3.3 Soft computing methods ....................................................................... 22 1.3.4 Discussions ........................................................................................... 25 1.4 Objectives of this study ................................................................................ 26 1.5 Outline of the thesis ..................................................................................... 27 1.6 List of references .......................................................................................... 31 Chapter 2 Available hydro-meteorological data and proposed river breakup forecasting model structure .............................................................. 43 2.1 Introduction .................................................................................................. 43 2.2 Study site and raw data collection ............................................................... 46 2.3 Development of index variables ................................................................... 50 2.3.1 Long lead-time variables ....................................................................... 51 2.3.2 Short lead-time variables .....................................................................
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