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|Title:||Towards Generic Real-Time Mapping Algorithms for Environmental Monitoring and Emergency Detection|
|Authors:||BRENNING Alexander; DUBOIS GREGOIRE|
|Citation:||STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT vol. 22 p. 601-611|
|Type:||Articles in periodicals and books|
|Abstract:||Real-time analysis of data reported by environmental monitoring networks poses a number of challenges, one of which is the conversion of point measurements of phenomena that display some spatial dependence into maps. This is the case for the many variables that cannot be monitored efficiently over large regions by satellites. Environmental pollutants, radiation levels, rainfall fields and seismic activity are but a few of these variables that are usually interpolated for the production of maps. These maps will then further serve as an essential support for decision-making. Ideally, in order to allow real-time assessments and minimize human intervention in case of hazards and emergencies that are frequently linked to the above mentioned variables (e.g. air pollution peaks, nuclear accidents, flash-floods, earthquakes), these maps should be established in near real-time and thus automatically. The ability of real-time mapping systems running in the routine mode to be able to cope with extreme events is not straightforward, and few systems are today used automatically for both monitoring the environment and triggering early warnings in case of necessity. Alternatively, adopting a decision-centered view of environmental monitoring and mapping systems allows us to re-formulate their final objective as a classification problem that consists of discriminating routine against emergency conditions, or background information against outliers. It is the purpose of this paper to give an overview of the main challenges for developing and evaluating automatic mapping systems for critical environmental variables, as well as to discuss steps toward the development of generic real-time mapping algorithms. Keywords: automatic real-time mapping, spatial interpolation, hot-spot detection, environmental monitoring|
|JRC Institute:||Sustainable Resources|
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