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Using Dempster–Shafer theory to model earthquake events

Author

Listed:
  • Marzieh Mokarram

    (Shiraz University)

  • Hamid Reza Pourghasemi

    (Shiraz University)

  • John P. Tiefenbacher

    (Texas State University)

Abstract

In this study, Dempster–Shafer theory (DST) is integrated into a geographic information system to model vulnerability of the land surface to earthquake events in northwestern Kermanshah Province, Iran, to predict where damage is most likely to occur. DST has never been used to spatially model earthquake vulnerability. To achieve this, data layers for several environmental attributes—aspect, elevation, lithology, slope angle, land use, distance from river courses, distance from roads, and distance from faults—were compiled in ArcGIS 10.2.2 software. Using membership functions, fuzzy maps were generated for each parameter. These fuzzy maps provided input data for the DST model. The predicted values were analyzed and compared at three confidence levels to determine the effectiveness of the model. The results are that 11.14%, 14.14%, and 17.18% (95%, 99%, and 99.5% confidence levels, respectively) of the study area are predicted to be susceptible to earthquakes based on receiver operating characteristic curves. The results also show that, according to the area under the curve (AUC) values (0.967, 0.828, and 0.849 for 95%, 99%, and 99.5% confidence levels, respectively), DST model generates earthquake zoning maps with high accuracy. Therefore, this model can be used for generating earthquake zoning maps with confidence levels that best suit the economic conditions and significance of the region.

Suggested Citation

  • Marzieh Mokarram & Hamid Reza Pourghasemi & John P. Tiefenbacher, 2020. "Using Dempster–Shafer theory to model earthquake events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 1943-1959, September.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:2:d:10.1007_s11069-020-04066-w
    DOI: 10.1007/s11069-020-04066-w
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    Cited by:

    1. Liming Gou & Jian Zhang & Naiwen Li & Zongshui Wang & Jindong Chen & Lin Qi, 2022. "Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.

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