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Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory

Thiesen, Stephanie; Darscheid, Paul; Ehret, Uwe

In this study, we propose a data-driven approach for automatically identifying rainfall-runoff events in discharge time series. The core of the concept is to construct and apply discrete multivariate probability distributions to obtain probabilistic predictions of each time step that is part of an event. The approach permits any data to serve as predictors, and it is non-parametric in the sense that it can handle any kind of relation between the predictor(s) and the target. Each choice of a particular predictor data set is equivalent to formulating a model hypothesis. Among competing models, the best is found by comparing their predictive power in a training data set with user-classified events. For evaluation, we use measures from information theory such as Shannon entropy and conditional entropy to select the best predictors and models and, additionally, measure the risk of overfitting via cross entropy and Kullback–Leibler divergence. As all these measures are expressed in “bit”, we can combine them to identify models with the best tradeoff between predictive power and robustness given the available data.

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Verlagsausgabe §
DOI: 10.5445/IR/1000091799
Veröffentlicht am 28.02.2019
DOI: 10.5194/hess-23-1015-2019
Zugehörige Institution(en) am KIT Institut für Wasser und Gewässerentwicklung (IWG)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 1027-5606, 1607-7938
KITopen-ID: 1000091799
Erschienen in Hydrology and earth system sciences
Band 23
Heft 2
Seiten 1015–1034
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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