Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS

Abstract

The 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.

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

“…By using Netica 5.18, which is a widely used Bayesian network development software and can be downloaded at https://www.norsys.com [55], we constructed the BN model, using the gradient ascent to learn the training set. The logarithmic loss, quadratic loss, and spherical payoff contained in the software were selected to preliminarily evaluate the error rate and scoring rules of the model [56]. The closer to zero for both logarithmic and quadratic loss, and the closer to 1 for the spherical payoff, the higher the accuracy of the model.…”

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models

“…It is evident that the spatial trend of the earthquake disaster chain hazard zone follows the distribution of elevation and slope. Zones of very high hazard were predominantly located within a 15-km radius of Tianchi in the previous work [34]. The main reason for the above differences is that the hazard intensity of the earthquake–collapse–landslide–debris flow disaster chain in previous work comes from the inference of BN; in this study, the hazard intensity analysis of volcanic earthquake–collapse–landslide disaster chain were obtained from the cumulative displacement calculated by the Newmark model.…”

“…The author’s previous work was mainly to construct the hazard assessment model of earthquake–collapse–landslide–debris flow disaster chain based on a BN from the perspective of every single disaster according to the hazard formation mechanism, which included chain probability and hazard intensity, and were obtained from inference of BN [34]. There are still some deficiencies in the previous works, first, the risk of the earthquake disaster chain was not considered, and the hazard discussed in the previous works was one factor in risk assessment elements.…”

Risk Assessment of Population Loss Posed by Earthquake-Landslide-Debris Flow Disaster Chain: A Case Study in Wenchuan, China

“…The earthquake-geological disaster chain is a common manifestation of multiple disasters, and it often causes serious damage due to the indirect strengthening effect of earthquakes that are extensive, persistent, and clustered [18][19][20]. Great earthquakes can not only cause a large number of landslides, but they can also aggravate the instability of slopes for a long time after the earthquake [7,11,12,21,22]. For example, there were about 5000 landslides in the Haiti earthquake on 12 January 2010 [21], and in the Maule earthquake there were 1226 landslides over an area of 120,500 km 2 and the rock mass was more fragmented [6].…”

“…Disaster chain risk assessment is a hot topic in multi-disaster research, and it is helpful for risk prevention and control [1,22,[35][36][37][38][39]. The disaster risk is dependent on the hazard, vulnerability, and exposure in terms of the population, ecology, and society, which have been widely used to effectively quantify the potential loss [37,[40][41][42].…”