A Gis-Based Multi-Criteria Evaluation Framework for Uncertainty Reduction in Earthquake Disaster Management Using Granular Computing

Abstract

One of the most important steps in earthquake disaster management is the prediction of probable damages which is called earthquake vulnerability assessment. Earthquake vulnerability assessment is a multicriteria problem and a number of multi-criteria decision making models have been proposed for the problem. Two main sources of uncertainty including uncertainty associated with experts‘ point of views and the one associated with attribute values exist in the earthquake vulnerability assessment problem. If the uncertainty in these two sources is not handled properly the resulted seismic vulnerability map will be unreliable. The main objective of this research is to propose a reliable model for earthquake vulnerability assessment which is able to manage the uncertainty associated with the experts‘ opinions. Granular Computing (GrC) is able to extract a set of if-then rules with minimum incompatibility from an information table. An integration of Dempster-Shafer Theory (DST) and GrC is applied in the current research to minimize the entropy in experts‘ opinions. The accuracy of the model based on the integration of the DST and GrC is 83%, while the accuracy of the single-expert model is 62% which indicates the importance of uncertainty management in seismic vulnerability assessment problem. Due to limited accessibility to current data, only six criteria are used in this model. However, the model is able to take into account both qualitative and quantitative criteria.

Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City

“…However, decision making for estimating building damage involves some uncertainties [22]. Limited number of studies have focused on dealing with intrinsic uncertainties of multiple decision making for assessing seismic vulnerability of buildings ([e.g., References [2,[23][24][25][26]). This paper focuses on studying one of the epistemic uncertainties in assessing seismic vulnerability of buildings namely inconsistency in a multi criteria decision making framework using fuzzy sets and Dempster-Shafer theories implemented on vector datasets.…”

Geospatial Data Utilisation in National Disaster Management Frameworks and the Priorities of Multilateral Disaster Management Frameworks: Case Studies of India and Bulgaria

“…Another group of studies research the geospatial data activities only of a category serving the specific purposes under DRM. It is argued by [51][52][53] that data collection and management are required for hazard, vulnerability, risk and capacity (HVRC) assessment. The need for data collection and management in the context of DRM for storing the datasets is also highlighted in [23,28], so that data can be efficiently retrieved through queries for editing or analysis.…”

Comparative analysis of disaster risk management practices in Bucharest, Ciudad de Mexico and Istanbul

“…Risk management plans have four stages: risk reduction, training, response and recovery (World Bank, 2011). Risk management is an integral part of urban crisis management (Cervelli et al, 2016) and many studies have been undertaken on the application of GIS in crisis management and modelling under different risk conditions (Armaş and Radulian, 2014;Nejad et al, 2015;Zhao and Liu, 2016;Khamespanah et al, 2016). Thus, GIS-based decision analysis techniques have been used in disaster assessment, risk prioritisation and land use planning (Ghajari et al, 2017).…”