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Variable optimization for flood prediction

Wilfried Segretier 1 Martine Collard 1 Laurent Brisson 2, 3 Jean-Émile Symphor 4
1 IDC
LAMIA - Laboratoire de Mathématiques Informatique et Applications
2 LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
3 Lab-STICC_TB_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 GRIMAAG - Groupe de Recherche en Informatique et Mathématiques Appliquées Antilles-Guyane
Abstract : In this paper, we present an heuristic based approach for feature selection in the context of flood prediction. Features are complex variables that represent aggregate values. We apply a preprocessing method on data in order to elicit relevant information that could not be easily accessible initially because it is split through several lines of a dataset. A genetic algorithm is used in order to search for the features that may prove the best performances for flood prediction.
Keywords : data mining genetic algorithms feature selection complex variables flood prediction
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Journal articles
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https://hal.archives-ouvertes.fr/hal-00840738
Contributor : Wilfried Segretier <>
Submitted on : Wednesday, July 3, 2013 - 5:56:21 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:21 PM

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CNRS | INSTITUT-TELECOM | UNIV-BREST | UNIV-AG | BNRMI | LAB-STICC | LAB-STICC_TB | ENIB | LAB-STICC_ENIB | LAMIA | IMT-ATLANTIQUE

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Wilfried Segretier, Martine Collard, Laurent Brisson, Jean-Émile Symphor. Variable optimization for flood prediction. Revue des Sciences et Technologies de l'Information - Série ISI : Ingénierie des Systèmes d'Information, Lavoisier, 2011, 16 (3), pp.113-139. ⟨10.3166/isi.16.3.113-139⟩. ⟨hal-00840738⟩

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