Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS
AbstractThe impacts of earthquakes and secondary disasters on ecosystems and the environment are attracting increasing global attention. Development of uncertainty reasoning models offers a chance to research these complex correlations. The primary aim of this research was to construct a disaster chain hazard assessment model that combines a Bayesian Network model and the ArcGIS program software for Changbai Mountain, China, an active volcano with a spate of reported earthquakes, collapses, and landslide events. Furthermore, the probability obtained by the Bayesian Networks was used to determine the disaster chain probability and hazard intensity of the earthquake events, while ArcGIS was used to produce the disaster chain hazard map. The performance of the Bayesian Network model was measured by error rate and scoring rules. The confirmation of the outcomes of the disaster chain hazard assessment model shows that the model demonstrated good predictive performance on the basis of the area under the curve, which was 0.7929. From visual inspection of the produced earthquake disaster chain hazard map, highly hazardous zones are located within a 15 km radius from the Tianchi center, while the northern and the western parts of the studied area are characterized mainly by “very low” to “low” hazard values. View Full-Text
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Han, L.; Zhang, J.; Zhang, Y.; Ma, Q.; Alu, S.; Lang, Q. Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS. ISPRS Int. J. Geo-Inf. 2019, 8, 210.
Han L, Zhang J, Zhang Y, Ma Q, Alu S, Lang Q. Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS. ISPRS International Journal of Geo-Information. 2019; 8(5):210.Chicago/Turabian Style
Han, Lina; Zhang, Jiquan; Zhang, Yichen; Ma, Qing; Alu, Si; Lang, Qiuling. 2019. "Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS." ISPRS Int. J. Geo-Inf. 8, no. 5: 210.
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