In the context of hazard monitoring, using sensor web technology to monitor anddetect hazardous conditions in near-real-time can result in large amounts of spatial data thatcan be used to drive analysis at an instrumented site. These data can be used for decisionmaking and problem solving, however as with any analysis problem the success ofanalyzing hazard potential is governed by many factors such as: the quality of the sensordata used as input; the meaning that can be derived from those data; the reliability of themodel used to describe the problem; the strength of the analysis methods; and the ability toeffectively communicate the end results of the analysis. For decision makers to make use ofsensor web data these issues must be dealt with to some degree. The work described in thispaper addresses all of these areas by showing how raw sensor data can be automaticallytransformed into a representation which matches a predefined model of the problem context.This model can be understood by analysis software that leverages rule-based logic andinference techniques to reason with, and draw conclusions about, spatial data. These toolsare integrated with a well known Geographic Information System (GIS) and existinggeospatial and sensor web infrastructure standards, providing expert users with the toolsneeded to thoroughly explore a problem site and investigate hazards in any domain.
Keywords: automated reasoning; hazard monitoring; sensor ontologies; sensor web infrastructure; spatial decision support systems.