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Landslide Risk Assessment by using a New Combination Model based on a Fuzzy Inference System Method

  • Construction Management
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Abstract

Landslides are one of the most dangerous phenomena that pose widespread damage to property and human lives. Over the recent decades, a large number of models have been developed for landslide risk assessment to prevent the natural hazards. These models provide a systematic approach to assess the risk value of a typical landslide. However, often models only utilize the numerical data to formulate a problem of landslide risk assessment and neglect the valuable information provided by experts’ opinion. This leads to an inherent uncertainty in the process of modelling. On the other hand, fuzzy inference systems are among the most powerful techniques in handling the inherent uncertainty. This paper develops a powerful model based on fuzzy inference system that uses both numerical data and subjective information to formulate the landslide risk more reliable and accurate. The results show that the proposed model is capable of assessing the landslide risk index. Likewise, the performance of the proposed model is better in comparison with that of the conventional techniques.

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References

  • Alfors, J. T., Burnett, J. L., and Gay, T. E. (1973). “Urban geology master plan for California-the nature, magnitude and costs of geologic hazards in California and recommendations for their mitigation: Bulletin 198.” California Division of Mines and Geology, Sacramento, California, pp. 112, https://doi.org/digitalcommons.law.ggu.edu/caldocs_agencies/256.

    Google Scholar 

  • Anbalagan, R., Kumar, R., Lakshmanan, K., Parida, S., and Neethu, S. (2015). “Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim.” Geoenvironmental Disasters, Vol. 2, No. 6, DOI: 10.1186/s40677-014-0009-y.

    Google Scholar 

  • Azimi, S. R. (2016). Soil slope stability techniques: A comprehensive analysis, A thesis submitted for Master of Philosophy (Civil Engineering), Curtin University.

    Google Scholar 

  • Azimi, S. R. and Nikraz, H. (2017). “Developing a model based on image processing for soil slope stability assessment.” Global Journal of Engineering Science and Researches, Vol. 4, No. 7, pp. 77–89, https://doi.org/www.gjesr.com/Issues%20PDF/Archive-2017/July-2017/9.pdf.

    Google Scholar 

  • Bai, S., Lu, P., and Wang, J. J. (2015). “Landslide susceptibility assessment of the Youfang catchment using logistic regression.” Journal of Mountain Science, Vol. 12, No. 4, pp. 816–827, DOI: 10.1007/s11629-014-3171-5.

    Article  Google Scholar 

  • Bobrowsky, P. and Highland, L. (2013). “The landslide handbook-a guide to understanding landslides: A landmark publication for landslide education and preparedness.” In: Sassa K., Rouhban B., Briceño S., McSaveney M., He B. (eds) Landslides: Global Risk Preparedness. Springer, Berlin, Heidelberg.

    Google Scholar 

  • Choi, J., Oh, H. J., Won, J. S., and Lee, S. (2010). “Validation of an artificial neural network model for landslide susceptibility mapping.” Environmental Earth Sciences, Vol. 60, No. 3, pp. 473–483, DOI: 10.1007/s12665-009-0188-0.

    Article  Google Scholar 

  • Daftaribesheli, A., Ataei, M., and Sereshki, F. (2011). “Assessment of rock slope stability using the Fuzzy Slope Mass Rating (FSMR) system.” Applied Soft Computing, Vol. 11, pp. 4465–4473, DOI: 10.1016/j.asoc.2011.08.032.

    Article  Google Scholar 

  • Davies, T. (2015). Landslide Hazards, Risks, and Disasters, Introduction. Shroder, J.F. (editor), Elsevier.

  • Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L., Arnold, M., Agwe, J., Buys, P., Kjevstad, O., Lyon, B., Yetman, G. (2005). Natural disaster hotspots: A global risk analysis, The World bank, Washington D.C

    Book  Google Scholar 

  • Ermini, L., Catani, F., and Casagli, N. (2005). “Artificial neural networks applied to landslide susceptibility assessment.” Geomorphology, Vol. 66, Nos. 1–4, pp. 327–343, DOI: 10.1016/j.geomorph.2004.09.025.

    Article  Google Scholar 

  • Grosan, C. and Abraham, A. (2011). Intelligent Systems: A Modern Approach, Springer-Verlag Berlin Heidelberg.

    Book  MATH  Google Scholar 

  • Guettouche, M. S. (2013). “Modeling and risk assessment of landslides using fuzzy logic. Application on the slopes of the Algerian Tell (Algeria).” Arab J Geosci, Vol. 6, pp. 3163–3173, DOI: 10.1007/s12517-012-0607-5.

    Article  Google Scholar 

  • Hamdia, K. M., Lahmer, T., Nguyen-Thoi, T., and Rabczuk, T. (2015). “Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS.” Computational Materials Science, Vol. 102, pp. 304–313, DOI: 10.1016/j.commatsci.2015.02.045.

    Article  Google Scholar 

  • Hamdia, K. M., Silani, M., Zhuang, X., He, P., and Rabczuk, T. (2017). “Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions.” International Journal of Fracture, Vol. 206, No. 2, pp. 215–227, DOI: 10.1007/s10704-017-0210-6.

    Article  Google Scholar 

  • Jamshidi, A., Yazdani-Chamzini, A., Yakhchali, S. H., and Khaleghi, S. (2013). “Developing a new fuzzy inference system for pipeline risk assessment.” Journal of Loss Prevention in the Process Industries, Vol. 26, pp. 197–208, DOI: 10.1016/j.jlp.2012.10.010.

    Article  Google Scholar 

  • Juventine, E. J. (2012). Landslide hazards: Household vulnerability, resilience and coping in Bududa district, eastern Uganda, Master Thesis in disaster management, University of the Free State.

    Google Scholar 

  • Kaya, I. and Çinar, D. (2008). “Facility location selection using a fuzzy outranking method.” Journal of Multiple-Valued Logic and Soft Computing, Vol. 14, pp. 251–263, DOI: 10.1142/9789812774118_0052.

    MathSciNet  MATH  Google Scholar 

  • Kaymak, U., Babuska, R., Setnes, M., Verbruggen, H. B., and Lemke, H. R. N. (1997). “Methods for simplification of fuzzy models.” Intelligent hybrid systems, Springer, Boston.

    Google Scholar 

  • Kjekstad, O. and Highland, L. (2009). “Economic and social impacts of landslides.” In K. Sassa & P. Canuti (Eds.), Landslides–Disaster Risk Reduction, Springer, Berlin, pp. 573–587.

    Google Scholar 

  • Klose, M. (2015). Landslide databases as tools for integrated assessment of landslide risk, Doctoral Thesis, University of Vechta, Germany. Springer International Publishing Switzerland.

    Book  Google Scholar 

  • Lari, S., Frattini, P., and Crosta, G. B. (2014). “A probabilistic approach for landslide hazard analysis.” Engineering Geology, Vol. 182, No. A, pp. 3–14, DOI: 10.1016/j.enggeo.2014.07.015.

    Article  Google Scholar 

  • Lee, E. M. and Jones, D. K. C. (2004). Landslide risk assessment, Thomas Telford Limited.

    Book  Google Scholar 

  • Leonardi, G., Palamara, R., and Cirianni, F. (2016). “Landslide susceptibility mapping using a fuzzy approach.” Procedia Engineering, Vol. 161, pp. 380–387, DOI: 10.1016/j.proeng.2016.08.578.

    Article  Google Scholar 

  • Mandal, S. and Maiti, R. (2015). “Geo-spatial variability of physiographic parameters and landslide potentiality (Chapter 2).” Semi-quantitative Approaches for Landslide Assessment and Prediction. Springer Science+Business Media Singapore.

    Chapter  Google Scholar 

  • Pradhan, B. and Abdulwahid, W. M. (2017). “Landslide risk assessment using multi-hazard scenario produced by logistic regression and LiDAR-Based DEM.” In: Pradhan B. (eds) Laser Scanning Applications in Landslide Assessment, Springer, pp. 253–275.

    Chapter  Google Scholar 

  • Pradhan, B., Oh, H. J., and Buchroithner, M. (2010). “Weights of-evidence model applied to landslide susceptibility mapping in a tropical hilly area.” Geomatics, Natural Hazards and Risk, Vol. 1, No. 3, pp. 199–223, DOI: 10.1080/19475705.2010.498151.

    Article  Google Scholar 

  • Raman, R. and Punia, M. (2012). “The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, Vol. 6, No. 3, pp. 145–161, DOI: 10.1080/17499518.2011.637504.

    Google Scholar 

  • Razani, M., Yazdani-Chamzini, A., and Yakhchali, S. H. (2013). “A novel fuzzy inference system for predicting roof fall rate in underground coal mines.” Safety Science, Vol. 55, pp. 26–33, DOI: 10.1016/j.ssci.2012.11.008.

    Article  Google Scholar 

  • Reger, J. P. (1979). “Discriminant analysis as a possible tool in landslide investigations.” Earth Surf. Process, Vol. 4, pp. 267–273, DOI: 10.1002/esp.3290040307.

    Article  Google Scholar 

  • Remondo, J., Bonachea, J., and Cendrero, A. (2005). “A statistical approach to landslide risk modelling at basin scale: From landslide susceptibility to quantitative risk assessment.” Landslides, Vol. 2, No. 4, pp. 321–328, DOI: 10.1007/s10346-005-0016-x.

    Article  Google Scholar 

  • Santacana, N., Baeza, B., Corominas, J., Paz, A. D., and Marturiá, J. (2003). “A GIS-Based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain).” Natural Hazards, Vol. 30, No. 3, pp. 281–295, DOI: 10.1023/B:NHAZ.0000007169.28860.80.

    Article  Google Scholar 

  • Sarkar, S., Roy, A. K., and Martha, T. R. (2013). “Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas.” Journal of the Geological Society of India, Vol. 82, No. 4, pp. 351–362, DOI: 10.1007/s12594-013-0162-z.

    Article  Google Scholar 

  • Shit, P. K., Bhunia, G. S., and Maiti, R. (2016). “Potential landslide susceptibility mapping using Weighted Overlay Model (WOM).” Modeling Earth Systems and Environment, Vol. 2, No. 21, DOI: 10.1007/s40808-016-0078-x.

    Google Scholar 

  • Shroder, J. F. and Davies, T. (2015). Landslide hazards, risks, and disasters, Hazards and Disasters Series, Elsevier Inc.

    Google Scholar 

  • Takagi, H. (1997). “Introduction to fuzzy systems, neural networks, and genetic algorithms.” Intelligent hybrid systems: Fuzzy logic, neural networks, and genetic algorithms (Edited by Da Ruan). Springer Science+Business Media, LLC.

    Google Scholar 

  • Tsangaratos, P. and Benardos, A. (2014). “Estimating landslide susceptibility through a artificial neural network classifier.” Natural Hazards, Vol. 74, No. 3, pp. 1489–1516, DOI: 10.1007/s11069-014-1245-x.

    Article  Google Scholar 

  • Vu-Bac, N., Lahmer, T., Zhuang, X., Nguyen-Thoi, T., and Rabczuk, T. (2016). “A software framework for probabilistic sensitivity analysis for computationally expensive models.” Advances in Engineering Software, Vol. 100, pp. 19–31, DOI: 10.1016/j.advengsoft.2016.06.005.

    Article  Google Scholar 

  • Yazdani-Chamzini, A. (2014). “Proposing a new methodology based on fuzzy logic for tunnelling risk assessment.” Journal of Civil Engineering and Management, Vol. 20, No. 1, pp. 82–94, DOI: 10.3846/13923730.2013.843583.

    Article  Google Scholar 

  • Yazdani-Chamzini, A., Razani, M., Yakhchali, S. H., Zavadskas, E. K., and Turskis, Z. (2013). “Developing a fuzzy model based on subtractive clustering for road header performance prediction.” Automation in Construction, Vol. 35, pp. 111–120, DOI: 10.1016/j.autcon.2013.04.001.

    Article  Google Scholar 

  • Yen, J. and Wang, L. (1999). “Simplifying fuzzy rule-based models using orthogonal transformation methods.” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), Vol. 1, No. 29. pp. 13–24, DOI: 10.1109/3477.740162.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). “Fuzzy sets.” Information and Control, Vol. 8, No. 3, pp. 338–353, DOI: 10.1016/S0019-9958(65)90241-X.

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh, L. A. (1996). “Fuzzy logic = computing with words.” IEEE Transactions on Fuzzy Systems, Vol. 4, No. 2, pp. 103–111, DOI: 10.1109/91.493904.

    Article  Google Scholar 

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Authors and Affiliations

  1. Dept. of Civil Engineering, Faculty of Science and Engineering, Curtin University, Curtin, Australia

    S. Reza Azimi & Hamid Nikraz

  2. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Abdolreza Yazdani-Chamzini

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  1. S. Reza Azimi
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  2. Hamid Nikraz
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  3. Abdolreza Yazdani-Chamzini
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Correspondence to Abdolreza Yazdani-Chamzini.

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Azimi, S.R., Nikraz, H. & Yazdani-Chamzini, A. Landslide Risk Assessment by using a New Combination Model based on a Fuzzy Inference System Method. KSCE J Civ Eng 22, 4263–4271 (2018). https://doi.org/10.1007/s12205-018-0041-7

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