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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.


Ontology Hierarchy.
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f1-sensors-08-00830: Ontology Hierarchy.

Mentions: The REASON (Real-time Evaluation Applying Sensor ONtologies) spatial decision support framework, which is described in detail in [6], is a tool which can be used to develop a spatial decision support system to monitor a user-defined domain. It is a platform for the evaluation of sensor data, assuming that the data are represented in an appropriate format (a concept discussed more thoroughly in Section 3). It was developed using the ArcAgents tool which bridges CLIPS, a programming language geared toward the development of expert systems, and ESRI's ArcGIS. REASON makes use of ontologies to partition and organize the knowledge it has about a given problem domain. Ontologies are often used where knowledge definition is a key component of the problem-solving process. One of the most general definitions of an ontology is a “specification of a conceptualization” [13]. By specifying the concepts relevant to a universe of interest, and the relationships between those concepts, a more formalized definition of a domain can be created. When the ontology is created in a machine-readable language, then software can be created that works with this stored knowledge to drive analysis methods. The ontological structure we use is a variant on one proposed by O'Brien and Gahegan [14], in which there are four separate but related ontologies which are used to contain all of the knowledge required by the system (Figure 1). These ontologies contain facts which describe the relevant concepts and objects in the problem domain, and rules which govern their behaviour. The “Spatial-Temporal Ontology” is the high-level ontology used to define foundational concepts such as geometry, topology, and temporal relationships. Two mid-level ontologies build on the concepts from the Spatial-Temporal ontology: the “Domain Ontology” and the “Sensor Ontology”. The domain ontology is used to describe the concepts related to the domain being observed, for example shear zones and downslope motion. The sensor ontology describes the sensors which are used to perform the observation. Finally, the low-level “Application Ontology” contains the concepts and logic related to the execution and capabilities of our given monitoring application. This includes the decision trees which govern the analysis of incoming and archived sensor data. The application ontology builds on the knowledge from the two mid-level (Sensor and Domain) ontologies, and thus from the spatial-temporal ontology as well.


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

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

f1-sensors-08-00830: Ontology Hierarchy.
Mentions: The REASON (Real-time Evaluation Applying Sensor ONtologies) spatial decision support framework, which is described in detail in [6], is a tool which can be used to develop a spatial decision support system to monitor a user-defined domain. It is a platform for the evaluation of sensor data, assuming that the data are represented in an appropriate format (a concept discussed more thoroughly in Section 3). It was developed using the ArcAgents tool which bridges CLIPS, a programming language geared toward the development of expert systems, and ESRI's ArcGIS. REASON makes use of ontologies to partition and organize the knowledge it has about a given problem domain. Ontologies are often used where knowledge definition is a key component of the problem-solving process. One of the most general definitions of an ontology is a “specification of a conceptualization” [13]. By specifying the concepts relevant to a universe of interest, and the relationships between those concepts, a more formalized definition of a domain can be created. When the ontology is created in a machine-readable language, then software can be created that works with this stored knowledge to drive analysis methods. The ontological structure we use is a variant on one proposed by O'Brien and Gahegan [14], in which there are four separate but related ontologies which are used to contain all of the knowledge required by the system (Figure 1). These ontologies contain facts which describe the relevant concepts and objects in the problem domain, and rules which govern their behaviour. The “Spatial-Temporal Ontology” is the high-level ontology used to define foundational concepts such as geometry, topology, and temporal relationships. Two mid-level ontologies build on the concepts from the Spatial-Temporal ontology: the “Domain Ontology” and the “Sensor Ontology”. The domain ontology is used to describe the concepts related to the domain being observed, for example shear zones and downslope motion. The sensor ontology describes the sensors which are used to perform the observation. Finally, the low-level “Application Ontology” contains the concepts and logic related to the execution and capabilities of our given monitoring application. This includes the decision trees which govern the analysis of incoming and archived sensor data. The application ontology builds on the knowledge from the two mid-level (Sensor and Domain) ontologies, and thus from the spatial-temporal ontology as well.

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.