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Streamflow forecasting using functional regression

Pierre Masselot 1 Sophie Dabo-Niang 2, 3 Fateh Chebana 1 Taha Ouarda 4
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 : Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
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https://hal.inria.fr/hal-01425931
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Submitted on : Wednesday, January 4, 2017 - 8:10:29 AM
Last modification on : Tuesday, December 7, 2021 - 1:14:02 PM

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Pierre Masselot, Sophie Dabo-Niang, Fateh Chebana, Taha Ouarda. Streamflow forecasting using functional regression. Journal of Hydrology, Elsevier, 2016, 538, pp.754-766. ⟨10.1016/j.jhydrol.2016.04.048⟩. ⟨hal-01425931⟩

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