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

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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.


Slope Monitoring Decision Tree.
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f6-sensors-08-00830: Slope Monitoring Decision Tree.

Mentions: Using the processing routine discussed in Section 2.2 (Figure 2), new values for each sensor are loaded from the Sensor Observation Service at each time step, and evaluation of these new values is performed using a decision tree which makes use of both the water table and slope motion measurements and the encoded expert knowledge to classify various sections (termed ‘rock masses’ here) of the slope according to subjective alert levels. The analysis proceeds according to the following simple steps (represented as a decision tree in Figure 6):


An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection
Slope Monitoring Decision Tree.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-08-00830: Slope Monitoring Decision Tree.
Mentions: Using the processing routine discussed in Section 2.2 (Figure 2), new values for each sensor are loaded from the Sensor Observation Service at each time step, and evaluation of these new values is performed using a decision tree which makes use of both the water table and slope motion measurements and the encoded expert knowledge to classify various sections (termed ‘rock masses’ here) of the slope according to subjective alert levels. The analysis proceeds according to the following simple steps (represented as a decision tree in Figure 6):

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.