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Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran

Author

Listed:
  • Kourosh Shirani

    (Isfahan Agricultural and Natural Resources Research and Education Center, AREEO)

  • Mehrdad Pasandi

    (University of Isfahan)

  • Alireza Arabameri

    (Tarbiat Modares University)

Abstract

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.

Suggested Citation

  • Kourosh Shirani & Mehrdad Pasandi & Alireza Arabameri, 2018. "Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran," 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. 93(3), pages 1379-1418, September.
  • Handle: RePEc:spr:nathaz:v:93:y:2018:i:3:d:10.1007_s11069-018-3356-2
    DOI: 10.1007/s11069-018-3356-2
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    1. F. Falaschi & F. Giacomelli & P. Federici & A. Puccinelli & G. D’Amato Avanzi & A. Pochini & A. Ribolini, 2009. "Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy," 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. 50(3), pages 551-569, September.
    2. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," 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. 65(1), pages 135-165, January.
    3. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," 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. 82(2), pages 1231-1258, June.
    4. L.P.H. Van Beek & Th.W.J Van Asch, 2004. "Regional Assessment of the Effects of Land-Use Change on Landslide Hazard By Means of Physically Based Modelling," 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. 31(1), pages 289-304, January.
    5. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," 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. 30(3), pages 451-472, November.
    6. L. Luzi & F. Pergalani, 1999. "Slope Instability in Static and Dynamic Conditions for Urban Planning: the ‘Oltre Po Pavese’ Case History (Regione Lombardia – Italy)," 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. 20(1), pages 57-82, July.
    7. Cheng Su & Lili Wang & Xizhi Wang & Zhicai Huang & Xiaocan Zhang, 2015. "Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine," 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. 76(3), pages 1759-1779, April.
    8. Vahid Nourani & Biswajeet Pradhan & Hamid Ghaffari & Seyed Sharifi, 2014. "Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models," 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. 71(1), pages 523-547, March.
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    3. Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data," 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. 107(2), pages 1427-1442, June.

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