Variable optimization for flood prediction

Wilfried Segretier; Martine Collard; Laurent Brisson; Jean-Émile Symphor

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

Document type :

Journal articles

Domain :

Computer Science [cs] / Artificial Intelligence [cs.AI]

Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00840738
Contributor : Wilfried Segretier <>
Submitted on : Wednesday, July 3, 2013 - 5:56:21 AM
Last modification on : Thursday, October 17, 2019 - 12:36:43 PM

Variable optimization for flood prediction

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Variable optimization for flood prediction

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