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River ice breakup forecasting using artificial neural networks and fuzzy logic systems Open Access


Other title
onset of breakup
River ice breakup forecasting
artificial neural networks
peak snowmelt runoff
fuzzy logic systems
Type of item
Degree grantor
University of Alberta
Author or creator
Zhao, Liming
Supervisor and department
Hicks, Faye (Civil and Envioronmental Engineering)
Robinson Fayek, Aminah (Civil and Envioronmental Engineering)
Examining committee member and department
Lye, Leonard (Civil Engineering of Memorial University)
Hicks, Faye (Civil and Envioronmental Engineering)
Ambtman, Karen Dow (Civil and Envioronmental Engineering)
Pedrycz, Witold (Electrical and Computer Engineering)
Loewen, Mark (Civil and Envioronmental Engineering)
Robinson Fayek, Aminah (Civil and Envioronmental Engineering)
Department of Civil and Environmental Engineering
Water Resources Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
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.
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.
Citation for previous publication
Zhao, L., Hicks, F and Robinson Fayek, A. 2012. Applicability of multilayer feed-forward neural networks to model the onset of river breakup. Journal of Cold Regions Science and Technology, 70 (2012): 32-42.Zhao, L., Hicks, F and Robinson Fayek, A. 2011. River breakup forecasting by hydro-meteorological data. Proceeding of the 20th Canadian Hydrotechnical Conference, Canadian Society for Civil Engineering, Ottawa, July 14-17, 2011, HY-048: 11pp.

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