Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology

Xu, Jing; Nyerges, Timothy L.; Nie, Guigen

doi:10.1080/13658816.2013.845893

Cited by 41 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we will explore the influence of secondary geological disasters on the estimation of human losses in mountainous areas. The automatic generation of earthquake response countermeasures using earthquake emergency response knowledge (Xu et al, 2014) from estimated earthquake disaster losses is another direction of study that will be pursued in the future.…”

Section: Discussionmentioning

confidence: 99%

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

Xu¹,

An²,

Nie³

2016

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Improving earthquake disaster loss estimation speed and accuracy is one of the key factors in effective earthquake response and rescue. The presentation of exposure data by applying a dasymetric map approach has good potential for addressing this issue. With the support of 30 × 30 areal exposure data (population and building data in China), this paper presents a new earthquake disaster loss estimation method for emergency response situations. This method has two phases: a pre-earthquake phase and a co-earthquake phase. In the pre-earthquake phase, we precalculate the earthquake loss related to different seismic intensities and store them in a 30 × 30 grid format, which has several stages: determining the earthquake loss calculation factor, gridding damage probability matrices, calculating building damage and calculating human losses. Then, in the co-earthquake phase, there are two stages of estimating loss: generating a theoretical isoseismal map to depict the spatial distribution of the seismic intensity field; then, using the seismic intensity field to extract statistics of losses from the pre-calculated estimation data. Thus, the final loss estimation results are obtained. The method is validated by four actual earthquakes that occurred in China. The method not only significantly improves the speed and accuracy of loss estimation but also provides the spatial distribution of the losses, which will be effective in aiding earthquake emergency response and rescue. Additionally, related pre-calculated earthquake loss estimation data in China could serve to provide disaster risk analysis before earthquakes occur. Currently, the precalculated loss estimation data and the two-phase estimation method are used by the China Earthquake Administration.

show abstract

Section: Discussionmentioning

confidence: 99%

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

Xu¹,

An²,

Nie³

2016

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Krzysztof Janowicz [5] proposed an observation-driven ontology engineering framework, which shows how its layers can be realized using specific methodologies, and relates the framework to existing work on geo-ontologies. Jinghai Xu,et al [6] proposed a conceptual model of knowledge for earthquake disaster emergency response (EDER). Geo-ontology is used to build basic modeling primitives and represent geospatial characteristics of the EDER knowledge.…”

Section: Related Workmentioning

confidence: 99%

World Geographical Ontology Model

Houby¹

2015

IJCA

View full text Add to dashboard Cite

Recent days have witnessed an explosive growth in generating data in geographic domain which leads to increasing the applications of geographical information system (GIS). Due to this increasing of GIS, GIS faced the problem of effective management of the spatial information from different resources and in different forms. So, Geographical ontologies have been introduced to the geographical domain not only as a concept model which can represent objects on semantic and knowledge level, but also to integrate geographical data from different sources and in different forms for reasoning and to facilitate knowledge sharing, in addition to assist in recognizing spatial terms.In this research, the World geographical ontology model has been developed to meet the needs of the recognition of the terminologies and the semantic relationships between geographical terms related to any location in the world. It will enable search engine to perform a spatially aware search. The design of World ontology model has been developed based on the analysis and finding relations between different parts of World locations to adopt ontology concepts. Logical formulation has been used to describe the ontology to be able to infer more intelligent relations. Then the model has been implemented using protégé tool, a sample of related data has been collected and finally the system has been tested. Query answers integrate data from different sources.

show abstract

“…Service resources include geospatial data services built on interoperable standards [15], e.g., the Web Feature Service (WFS) [16] for geographical features, the Web Map Service (WMS) [17] for geo-registered map images, the Web Coverage Service for raster data, the Sensor Observation Service (SOS) [18] for near-real-time sensor observations, the Web Processing Service (WPS) [19] for encapsulating analysis and decision models for smart cities, the Sensor Event Service (SES) [20] for filtering of and subscription to sensor observations (events), and the Web Notification Service (WNS) [21] for message notification. Meanwhile, various public emergencies, such as security incidents, transportation accidents, and accidents involving public facilities and equipment, occur frequently and can result in heavy casualties and economic losses [22]. These rich information resources can be utilized to assist smart-city decision-makers in coping with public emergencies and making decisions.…”

Section: Introductionmentioning

confidence: 99%

Event-Driven Distributed Information Resource-Focusing Service for Emergency Response in Smart City with Cyber-Physical Infrastructures

Xiao

Chen

Gong

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:The smart city has become a popular topic of investigation. How to focus large amounts of distributed information resources to efficiently cope with public emergencies and provide support for personalized decision-making is a vitally important issue in the construction of smart cities. In this paper, an event-driven focusing service (EDFS) method that uses cyber-physical infrastructures for emergency response in smart cities is proposed. The method consists of a focusing service model at the top level, an informational representation of the model and a focusing service process to operate the service model in emergency response. The focusing service method follows an event-driven mechanism that allows the focusing service process to be triggered by public emergencies sensed by wireless sensor networks (WSNs) and mobile crowd sensing, and it integrates the requirements of different societal entities with regard to response to emergencies and information resources, thereby providing comprehensive and personalized support for decision-making. Furthermore, an EDFS prototype system is designed and implemented based on the proposed method. An experiment using a real-world scenario-the gas leakage in August 2014 in Taiyuan, China-is presented demonstrating the feasibility of the proposed method for assisting various societal entities in coping with and efficiently responding to public emergencies.

show abstract

Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology

Cited by 41 publications

References 19 publications

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

World Geographical Ontology Model

Event-Driven Distributed Information Resource-Focusing Service for Emergency Response in Smart City with Cyber-Physical Infrastructures

Contact Info

Product

Resources

About