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GIS-based fuzzy-analytic network process (FAHP), fuzzy-analytic hierarchy process (FANP) methods and feature selection algorithm (FSA) to determine earthquake-prone areas in Kermanshah Province

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Abstract

Earthquakes have caused considerable economic damage and loss of lives. The ability to determine earthquake-prone areas has utmost importance to crisis management. Given the gravity of the issue, this research proposes to incorporate geographic information system-based fuzzy-analytic network process (FAHP) and fuzzy-analytic hierarchy process (FANP) methods to determine earthquake-prone areas in the southwest of Iran. In addition, this paper is to utilize the feature selection algorithm (FSA) to select the most relevant parameters influencing earthquakes and prepare seismic activity maps. The final maps obtained using the relevant data were compared with maps obtained using the entire data. For this aim, a fuzzy membership function was initially employed to generate a fuzzy map for different layers, after which the analytic network process and analytic hierarchy process methods were utilized for assigning proper weights to each layer and eventually procuring earthquake maps. The input information for the mentioned methods consisted of landscape, slope, elevation, lithology, land use, soil, road, and fault maps. According to the FSA approach, the most relevant parameters include distance to fault (DTF), lithology, slope, and landscape. The results of FANP and FAHP methods utilizing the receiver-operating characteristic curve showed that the FANP method has a higher accuracy for determining earthquake-prone areas. As the areas located in the northern parts of the region are more at risk. In addition, Finally, the results of earthquake maps obtained using solely the relevant data from FSA were compared with the maps obtained using the entire data. The results of FSA showed that the Best first and Greedy-Stepwise models have high accuracy (R = 0.99) and the most important data for determining earthquake-prone areas using these models are DTF, lithology, slope, aspect. Therefore, by saving time and money, it is possible to prepare maps of earthquake-prone areas with high accuracy.

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Abbreviations

μ A :

Membership function

m :

Number of columns

n :

Number of rows

a ij :

Element in the ith row and jth column of the pairwise matrix

r ij :

Corresponding value in the normalized matrix

w i :

Weight assigned to the ith option

v j :

Final score for the jth option

w k :

Weight for each parameter

g ij :

Corresponding weight relevant

RAEi :

Relative absolute error

N :

Total number of data

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Acknowledgements

The authors would like to thank Shiraz University for providing financial support (238726-1001) for this study.

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Authors and Affiliations

  1. Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, 71946-84471, Shiraz, Iran

    Marzieh Mokarram

  2. Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran

    Saeed Negahban

  3. Science and Technology Faculty, University of Abu bakr belkaid Tlemcen, B.P. 119, 13000, Tlemcen, Algeria

    Belkendil Abdeldjalil

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  1. Marzieh Mokarram
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  2. Saeed Negahban
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  3. Belkendil Abdeldjalil
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Correspondence to Marzieh Mokarram or Saeed Negahban.

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Mokarram, M., Negahban, S. & Abdeldjalil, B. GIS-based fuzzy-analytic network process (FAHP), fuzzy-analytic hierarchy process (FANP) methods and feature selection algorithm (FSA) to determine earthquake-prone areas in Kermanshah Province. Environ Earth Sci 80, 633 (2021). https://doi.org/10.1007/s12665-021-09934-7

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