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
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;
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on
Conference_Location :
Paris
DOI :
10.1109/VAST.2014.7042488