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An evolutionary data mining approach on hydrological data with classifier juries

Wilfried Segretier 1 Manuel Clergue 1 Martine Collard 1 Luis Izquierdo 2
LAMIA - Laboratoire de Mathématiques Informatique et Applications
Abstract : In this paper, we present an evolutionary approach for extracting a model of flood prediction from hydrological data observed timely on water heights in a river watershed. Since this kind of data recorded by sensors on river basins is highly scarce and hopefully much unbalanced between cases of floods and non-floods, we have adopted the notion of aggregate variables which values are computed as aggregates on raw data. An evolutionary algorithm is involved to allow selecting the best sets - juries of classifiers- of such variables as predictive variables. Two real hydrological data sets are trained and they both show the efficiency of the method compared to traditional solutions for prediction.
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Contributor : Wilfried Segretier <>
Submitted on : Wednesday, July 3, 2013 - 6:11:49 AM
Last modification on : Wednesday, June 3, 2020 - 11:10:04 PM

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Wilfried Segretier, Manuel Clergue, Martine Collard, Luis Izquierdo. An evolutionary data mining approach on hydrological data with classifier juries. IEEE Congress on Evolutionary Computation 2012, Jun 2013, Brisbane, Australia. pp.1-8, ⟨10.1109/CEC.2012.6252897⟩. ⟨hal-00840739⟩



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