A spatial-based KDD process to better manage the river water quality

Hugo Alatrista-Salas, Frédéric Flouvat, Sandra Bingay, Nazha Selamoui-Folcher, Maguelonne Teisseire

Producción científica: Contribución a una revistaArtículo de revista


Rapid population growth and human activities (such as agriculture, industry, transports...) development have increased vulnerability risk for water resources. Due to the nature of river networking distribution, the interactions between hydrosystems and human pressures are difficult understand. In addition, many hypotheses about river water pollution can be formulated. In this context, knowledge discovery is a promising process to better understand and manage such phenomenon. We have combined the results of several data mining methods to extract actionable knowledge from data collected by stations located along several rivers. First, data are pre processed (aggregated) according to different spatial relationships, which leads to the extraction of semantically different patterns in the second phase of the process. Then, the resulting datasets are mined to extract sequential and spatio-sequential patterns. Finally, patterns are filtered using a new quality measure based on the notion of contradiction. Such elements can be used to assess specialized indicators to assist the experts in river water quality restoration.
Título traducido de la contribuciónUn processus d'ECD spatial pour une meilleure gestion de la qualité de l'eau des rivières
Idioma originalInglés
Páginas (desde-hasta)469-494
PublicaciónRevue internationale de géomatique
EstadoPublicada - 2013

Palabras clave

  • data mining
  • sequential patterns
  • hydro-biological data
  • spatiotemporal data

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