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 multi-criteria 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 DempsterShafer 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.

Keywords:

• Multi-Criteria Decision Making
• GIS
• disaster management
• granular computing
• earthquake vulnerability assessment
• Dempster-Shafer Theory

Additional information

Notes on contributors

Fatemeh Khamespanah

Fatemeh KHAMESPANAH. Received her Bachelor’s degree in surveying engineering at the University of Tehran and Master’s degree in Geospatial information systems Engineering from the university of Tehran. Her research interests are disaster management, granular computing, artificial intelligence and earthquake vulnerability assessment.

Mahmoud Reza Delavar

Mahmoud Reza DELAVAR. Received his PhD in Geomatic Eng. from University of New South Wales (UNSW), Australia, and now is an Associate Professor in GIS and Land Administration and Director of GIS WG in Center of Excellence in Geomatic Engineering in Disaster Management, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran.

In addition, he is the national representative in Urban Data Management Society (UDMS) and was the Scientific Secretary of ISPRS WG II/IV (Uncertainty modeling and quality control for spatial data) during 2008–2012. He is a reviewer of Spatial Statistics and ISPRS International journal of Geo Information. His research interests are spatial data quality, spatio-temporal GIS, UBGIS, land administration, SDI, urban GIS modeling, and disaster management.

Milad Moradi

Milad MORADI. Graduated from the University of Tehran in Surveying Engineering in 2011. He also finished his Master’s studies in Geospatial Information Science in 2014 at the University of Tehran. His research interests include volunteered geospatial information, smart cities, multi criteria decision making and disaster management.

Hossein Sheikhian

Hossein SHEIKHIAN. Received his BSc. and MSc. in Geospatial information systems Engineering from university of Tehran. His research interests are urban planning, disaster management, artificial intelligence and environmental modelling. He published papers on seismic hazard assessment, air pollution, urban land use planning and geosensor networks.