Earthquake Disaster Management and Insurance

As one of the most powerful tools to reduce the earthquake loss, the Earthquake Disaster Management [EDM] and Insurance [EI] have been highlighted and have had a great progress in many countries in recent years. Earthquake disaster management includes a series of contents, such as earthquake hazard and risk analysis, vulnerability analysis of building and infrastructure, earthquake aware training, and building the emergency response system. EI, which has been included in EDM after this practice has been...

Earthquake Disaster Risk Reduction in Iran:Lessons and "Lessons Learned" from Three Large Earthquake Disasters——Tabas 1978,Rudbar 1990,and Bam 2003

This article addresses three large earthquake disasters in Iran: Tabas in 1978, Rudbar in 1990, and Bam in 2003. Lessons and 'Lessons Learned' from these three earthquake disasters were investigated together with their contributions over time towards earthquake disaster risk reduction in Iran. Many lessons from 1978 Tabas, 1990 Rudbar, and 2003 Bam did not become 'Lessons Learned' and they were identified again within the dramatic context of other earthquake disasters in various places of Iran. Both lessons and 'Lessons Learned' from Tabas, Rudbar, Bam,and other earthquake disasters in Iran require a sustainable long-term framework—an earthquake culture.

Preliminary identification of earthquake triggered multi-hazard and risk in Pleret Sub-District(Yogyakarta,Indonesia)

Yogyakarta is one of the large cities in Central Java,located on Java Island,Indonesia.The city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several seismically active zones,such as the Sunda Megathrust and the active fault known as the Opak Fault.Since a devastating earthquake of 2006,the population of the Pleret sub-district has increased significantly.Thus,the housing demand has increased,and so is the pace of low-cost housing that does not meet earthquake-safety requirements,and furthermore are often located on unstable slopes.The local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of earthquakes.Within this context,this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction,and coseismic landslides.To achieve this,we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain parameters.The results have revealed the presence of numerous uncharted active faults with movements visible from imagery and outcrops.show that the middle part of the study area has a complex geological structure,indicated by many unchartered faults in the outcrops.Using this newly mapped blocks combined with shear strain data,we reassessed the collapse probability of buildings that reach level>0.75 near the Opak River,in central Pleret sub-district.Classifying the buildings and from population distribution,we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing buildings.The secondary hazards follow a slightly different distribution with a concentration of risks in the West.

Calculation of landslide occurrence probability in Taiwan region under different ground motion conditions

In this study,Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region.The analyses were based on the 1999 Taiwan Chi-Chi Earthquake,the largest earthquake in the history in this Region in a hundred years,thus can provide better control on the prediction accuracy of the model.This seismic event has detailed and complete seismic landslide inventories identified by polygons,including 9272 seismic landslide records.Taking into account the real earthquake landslide occurrence area,the difference in landslide area and the non-sliding/sliding sample ratios and other factors,a total of 13,656,000 model training samples were selected.We also considered other seismic landslide influencing factors,including elevation,slope,aspect,topographic wetness index,lithology,distance to fault,peak ground acceleration and rainfall.Bayesian probability method and machine learning model were combined to establish the multi-factor influence of earthquake landslide occurrence model.The model is then applied to the whole Taiwan region using different ground motion peak accelerations(from 0.1 g to 1.0 g with 0.1 g intervals)as a triggering factor to complete the real probability of earthquake landslide map in Taiwan under different peak ground accelerations,and the functional relationship between different Peak Ground Acceleration and their predicted area is obtained.

Location of Disaster Management Bases Using Spatial Analysis

Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability.Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt,resulting in an average death rate six times higher than the global average during earthquakes.Therefore,selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness.When selecting the placement of rescue centers,accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas.The location of these centers does not require any particular combinations.To address this issue,a study was conducted utilizing GIS,artificial neural networks,fuzzy logic,and mathematical models to determine the optimal placement based on 12 indicators within two clusters:natural and human.To examine the information layers of the initial stage,a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS.Using functions and algorithms such as Fuzzy Logic in IDRISI,TOPSIS,and VIKOR software,the layers were assessed for weightage before being overlaid.The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models.Notably,the areas with high desirability varied to a greater extent:the fuzzy model varied by 9.3%,neural network by 12.4%,VIKOR by 4.5%,and TOPSIS by 16.2%.The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model.Additionally,the study yielded other significant findings such as the correlation between study area size and model accuracy.Specifically,smaller study areas exhibited higher model accuracy.The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy.As a result,employing these models in crisis management planning,particularly in pre-crisis management for identifying rescue center locations,would be highly advantageous and increase the precision of these endeavors.

A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas

Urbanization can be a challenge and an opportunity for earthquake risk mitigation.However,little is known about the changes in exposure(for example,population and urban land)to earthquakes in the context of global urbanization,and their impacts on fatalities in earthquake-prone areas.We present a global analysis of the changes in population size and urban land area in earthquake-prone areas from 1990 to 2015,and their impacts on earthquake-related fatalities.We found that more than two thirds of population growth(or 70%of total population in 2015)and nearly three quarters of earthquake-related deaths(or 307,918 deaths)in global earthquake-prone areas occurred in developing countries with an urbanization ratio(percentage of urban population to total population)between 20 and 60%.Holding other factors constant,population size was significantly and positively associated with earthquake fatalities,while the area of urban land was negatively related.The results suggest that fatalities increase for areas where the urbanization ratio is low,but after a ratio between 40 and 50%occurs,earthquake fatalities decline.This finding suggests that the resistance of building and infrastructure is greater in countries with higher urbanization ratios and highlights the need for further investigation.Our quantitative analysis is extended into the future using Shared Socioeconomic Pathways to reveal that by 2050,more than 50%of the population increase in global earthquake-prone areas will take place in a few developing countries(Pakistan,India,Afghanistan,and Bangladesh)that are particularly vulnerable to earthquakes.To reduce earthquake-induced fatalities,enhanced resilience of buildings and urban infrastructure generally in these few countries should be a priority.