GeoENV, the Conference on Geostatistics for Environmental Applications, shows the state of the art of geostatistics in environmental applications with new cases, results and relevant discussions from leading researchers and practitioners around the world. Novel and outstanding theoretical and practical developments of geostatistics in the environmental field have been compiled from three main areas: that of Hydrology, another regarding Groundwater and Groundwater Contamination, and a third on Soil Contamination and Site Remediation Air Pollution, Ecology and Other Applications.
GeoENV conferences have been held biannually at venues across Europe. From the first conference in Lisbon in 1996, the event has been staged in Valencia (1998), Avignon (2000), Barcelona (2002), Neuchâtel (2004), Rhodes (2006), Southampton (2008), Gent (2010) and has become established as a leading forum for scientists across a broad range of disciplines to share their experiences on the application of geostatistics to environmental problems.
Participants will be made familiar with the latest methodological tools and methods, stochastic simulation techniques, models of integrating soft information (seismic and remote sensing images), inverse modelling of groundwater flow, neural network classification or change of support and up-scaling. They will be able to share ideas on topics as diverse as methodological developments, applications in the soil sciences, climatology, pollution, health, wildlife mapping, fisheries and remote sensing, among other areas. With its focus on environmental applications of geostatistics, rather than the more traditional geostatistical realm of mining and petroleum exploration. Geostatistical tools and approaches used to successfully resolve a variety of specific problems in environment modelling will be shown, especially those resulting from the typical scarcity of spatial sampling, the time component of very dynamic systems, the modelling of various systems of contaminants or the uncertainty assessment of health cost functions.