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Change point detection of flood events using a functional data framework

Mohamed Ali Ben Alaya 1 Camille Ternynck 2 Sophie Dabo-Niang 3 Fateh Chebana 1 Taha Ouarda 1
3 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.
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Submitted on : Sunday, February 7, 2021 - 11:27:50 AM
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Mohamed Ali Ben Alaya, Camille Ternynck, Sophie Dabo-Niang, Fateh Chebana, Taha Ouarda. Change point detection of flood events using a functional data framework. Advances in Water Resources, Elsevier, 2020, 137, pp.103522. ⟨10.1016/j.advwatres.2020.103522⟩. ⟨hal-03133809⟩



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