Risk Assessment Using Spatial Prediction Model for Natural Disaster Preparedness

Chung, Chang-Jo; Fabbri, Andrea G.; Jang, Dong Ho; Scholten, H.J.

doi:10.1007/3-540-27468-5_45

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Chung and Fabbri (1999) used Bayesian prediction models for landslide hazard mapping. Chung et al (2005) developed a three-stage procedure in spatial data analysis to estimate the probability of the occurrence of the natural hazardous events and to evaluate the uncertainty of the estimators of that probability. Chang et al (2007) used generalized positive Boolean functions for landslide classification.…”

Section: Clustering/classificationmentioning

confidence: 99%

Survey of data management and analysis in disaster situations

Hristidis

Chen

et al. 2010

Journal of Systems and Software

195

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Section: Clustering/classificationmentioning

confidence: 99%

Survey of data management and analysis in disaster situations

Hristidis

Chen

et al. 2010

Journal of Systems and Software

195

View full text Add to dashboard Cite

“…Integration of knowledge There are many class-to-class relationships in the strategy response ontology model (as presented in Table 1), and these relationships make risk response strategies closely linked. The result of the response strategy is summarized as mitigation, avoidance or transfer of MSW risk [56,57].…”

Section: Rule Extractionmentioning

confidence: 99%

Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning

Qing

Chen

Liu

et al. 2020

IJERPH

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Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response.

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“…This is obtained by means of interactive exercises and experiments using Spatial Target Mapping software and Spatial Risk Assessment software [17], in conjunction with a general-purpose spreadsheet and optionally a geographic information system. Fabbri et al [18] and Chung et al [19] discussed earlier versions of the software. However, to avoid having to become familiar with the peculiarities of a specific system, procedural steps are documented in guided exercises that help in constructing the predictions and in developing the different strategies.…”

Section: Tools and Techniquesmentioning

confidence: 99%

Training decision-makers in hazard spatial prediction and risk assessment: ideas, tools, strategies and challenges

Fabbri

Chung

2009

WIT Transactions on the Built Environment

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Hazard prediction and risk assessment over regions exposed to natural and technological processes are complex tasks that require exposure to quantization of its uncertainty related to the prediction of future events through statistical methods, spatial data analysis, case studies and process evolution interpretation in conditions of uncertainty. All too often decision makers, DMs, similarly to judges in environmental legal practice, do not have technical training to enable them to communicate/understand the associated uncertainty from technical specialists. In particular communication is a challenge with those who can provide prediction maps and associated statistics to support decisions on disaster prevention, avoidance or mitigation. An interactive short course was prepared to overcome such obstacles to responsible land use planning and proactive measure taking, for example, by asking a set of questions. A first phase in the training follows steps that are to facilitate the comprehension of a spatial database on landslide hazard, of its data processing, and of the interpretation of the analysis results. Integral parts of a second phase are the theory of predictive methods, the strategy in prediction map generation and visualization, including validation via blind tests and the representation of the associated spatial and prediction uncertainties. A successive third phase of the training brings in environmental and socioeconomic spatial indicators to assign vulnerabilities and values to exposed elements in the spatial database. Scenarios for hazard development in the future are then provided. They allow to estimate the uncertainty associated with the probabilities of hazardous occurrences and to resolve the risk equation for different settings. The DM training course includes interactive and iterative

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Risk Assessment Using Spatial Prediction Model for Natural Disaster Preparedness

Cited by 10 publications

References 11 publications

Survey of data management and analysis in disaster situations

Survey of data management and analysis in disaster situations

Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning

Training decision-makers in hazard spatial prediction and risk assessment: ideas, tools, strategies and challenges

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