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dc.contributor.advisorKhan, Usman
dc.contributor.authorSnieder, Everett Joshua
dc.date.accessioned2019-11-22T18:57:46Z
dc.date.available2019-11-22T18:57:46Z
dc.date.issued2019-11-22
dc.identifier.urihttp://hdl.handle.net/10315/36792
dc.description.abstractFloods are the most frequent and costly natural disaster in Canada. Flow forecasting models can be used to provide an advance warning of flood risk and mitigate flood damage. Data-driven models have proven to be suitable for flow forecasting applications, yet there are several outstanding challenges associated with model development. Firstly, this research compares four methods for input variable selection for data-driven models, which are used to minimize model complexity and improve performance. Next, methods for reducing the temporal error for data-driven flood forecasting models are investigated. Two procedures are proposed to minimize timing error: error weighting and least-squares boosting. A class of performance measures called visual measures is used to discriminate between timing and amplitude errors, and hence quantifying the impacts of each correction procedure. These studies showcase methods for improving the performance of flow forecasting models, more reliable flood risk predictions, and better preparedness for flood events.
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectCivil engineering
dc.titleArtificial Neural Network-Based Flood Forecasting: Input Variable Selection and Peak Flow Prediction Accuracy
dc.typeElectronic Thesis or Dissertation
dc.degree.disciplineCivil Engineering
dc.degree.nameMASc - Master of Applied Science
dc.degree.levelMaster's
dc.date.updated2019-11-22T18:57:45Z
dc.subject.keywordsflow forecasting
dc.subject.keywordsflooding
dc.subject.keywordsartificial neural networks
dc.subject.keywordsmachine learning
dc.subject.keywordsinput variable selection
dc.subject.keywordspartial correlation
dc.subject.keywordspartial mutual information
dc.subject.keywordsinput omission
dc.subject.keywordsneural pathway strength analysis
dc.subject.keywordstiming error
dc.subject.keywordsamplitude error
dc.subject.keywordsleast-squares boosting
dc.subject.keywordserror weighting
dc.subject.keywordsvisual performance measures
dc.subject.keywordspeak difference
dc.subject.keywordshydrograph matching
dc.subject.keywordsseries distance


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