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An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection

View Article: PubMed Central - PubMed

ABSTRACT

In the context of hazard monitoring, using sensor web technology to monitor and detect hazardous conditions in near-real-time can result in large amounts of spatial data that can be used to drive analysis at an instrumented site. These data can be used for decision making and problem solving, however as with any analysis problem the success of analyzing hazard potential is governed by many factors such as: the quality of the sensor data used as input; the meaning that can be derived from those data; the reliability of the model used to describe the problem; the strength of the analysis methods; and the ability to effectively communicate the end results of the analysis. For decision makers to make use of sensor web data these issues must be dealt with to some degree. The work described in this paper addresses all of these areas by showing how raw sensor data can be automatically transformed 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 and inference techniques to reason with, and draw conclusions about, spatial data. These tools are integrated with a well known Geographic Information System (GIS) and existing geospatial and sensor web infrastructure standards, providing expert users with the tools needed to thoroughly explore a problem site and investigate hazards in any domain.

No MeSH data available.


Simulated slope model showing water levels and their relationship to the motion of the slope.
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f7-sensors-08-00830: Simulated slope model showing water levels and their relationship to the motion of the slope.

Mentions: Figure 7 shows the REASON slope monitoring system in action. The small red and white dots represent inclinometer measurement locations: the red dots indicate “Active” sensors and the white dots indicate “Inactive” sensors for a given time step. The blue dots represent water table readings at six standpipe locations. The coloured portions of the slope indicate alert levels for the various regions of the slope as classified by the decision tree in Figure 6. The integration of hydrologic and geotechnical measurements with domain knowledge about how slopes behave under evolving slope conditions is made possible through the use of ontologies. Adding the spatial analysis capabilities of the GIS allows us to relate the position of the water table with the active features within the slope, and since we have previously determined that this relationship is significant then we can build our decision tree in such a way that it can use both data sets even though they were never built specifically to be integrated, and may in fact be operated as separate sensor networks by independent organizations.


An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection
Simulated slope model showing water levels and their relationship to the motion of the slope.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927503&req=5

f7-sensors-08-00830: Simulated slope model showing water levels and their relationship to the motion of the slope.
Mentions: Figure 7 shows the REASON slope monitoring system in action. The small red and white dots represent inclinometer measurement locations: the red dots indicate “Active” sensors and the white dots indicate “Inactive” sensors for a given time step. The blue dots represent water table readings at six standpipe locations. The coloured portions of the slope indicate alert levels for the various regions of the slope as classified by the decision tree in Figure 6. The integration of hydrologic and geotechnical measurements with domain knowledge about how slopes behave under evolving slope conditions is made possible through the use of ontologies. Adding the spatial analysis capabilities of the GIS allows us to relate the position of the water table with the active features within the slope, and since we have previously determined that this relationship is significant then we can build our decision tree in such a way that it can use both data sets even though they were never built specifically to be integrated, and may in fact be operated as separate sensor networks by independent organizations.

View Article: PubMed Central - PubMed

ABSTRACT

In the context of hazard monitoring, using sensor web technology to monitor and detect hazardous conditions in near-real-time can result in large amounts of spatial data that can be used to drive analysis at an instrumented site. These data can be used for decision making and problem solving, however as with any analysis problem the success of analyzing hazard potential is governed by many factors such as: the quality of the sensor data used as input; the meaning that can be derived from those data; the reliability of the model used to describe the problem; the strength of the analysis methods; and the ability to effectively communicate the end results of the analysis. For decision makers to make use of sensor web data these issues must be dealt with to some degree. The work described in this paper addresses all of these areas by showing how raw sensor data can be automatically transformed 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 and inference techniques to reason with, and draw conclusions about, spatial data. These tools are integrated with a well known Geographic Information System (GIS) and existing geospatial and sensor web infrastructure standards, providing expert users with the tools needed to thoroughly explore a problem site and investigate hazards in any domain.

No MeSH data available.