• DocumentCode
    119482
  • Title

    HydroQual: Visual analysis of river water quality

  • Author

    Accorsi, Pierre ; Lalande, Nathalie ; Fabregue, Mickael ; Braud, Agnes ; Poncelet, Pascal ; Sallaberry, Arnaud ; Bringay, Sandra ; Teisseire, Maguelonne ; Cernesson, Flavie ; Le Ber, Florence

  • Author_Institution
    LIRMM, Univ. Montpellier 2, Montpellier, France
  • fYear
    2014
  • fDate
    25-31 Oct. 2014
  • Firstpage
    123
  • Lastpage
    132
  • Abstract
    Economic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40], River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56], Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotemporal data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts.
  • Keywords
    data mining; data visualisation; ecology; environmental science computing; river pollution; statistical analysis; temporal databases; visual databases; water quality; water resources; HydroQual tool; Rhine river; agriculture expansion; aquatic ecosystems preservation; aquatic ecosystems restoration; economic development; emblematic ecological disasters; environmental issue; government initiatives; industrialization; population growth; public health; river pollution; river water quality; spatiotemporal data mining; statistical approaches; visual analysis; visualization techniques; water abstraction; water managers; water quality degradation; water resources; Biology; Data mining; Data visualization; Databases; Rivers; Water pollution; Water resources; Spatiotemporal Data Mining and Visualization; Visual Analytics; Water Quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on
  • Conference_Location
    Paris
  • Type

    conf

  • DOI
    10.1109/VAST.2014.7042488
  • Filename
    7042488