COMBINING FUZZY LOGIC AND INFORMATION THEORY FOR PRODUCING A LANDSLIDE SUSCEPTIBILITY MODEL


Published: Jul 27, 2016
Keywords:
slope stability fuzzy weighting Shannon’s entropy index Tzoumerka Greece
P. Tsangaratos
I. Ilia
Abstract

The main objective of the present study was to develop a landslide susceptibility model by combining Fuzzy logic and Information Theory in order to estimate the spatial probability of landslide manifestation, in the mountains of central Tzoumerka, Greece. Specifically, Fuzzy logic was enabled for weighting the landslide related variables based on expert knowledge and in respect to landslide susceptibility, while the Shannon’s entropy index, an index from Information Theory, was calculated to weight the significance of each landslide related variable based on the available data. The final landslide susceptibility map was produced by applying the weighted sum method. Engineering lithological units, slope angle, slope aspect, distance from tectonic features, distance from river network and distance from road network were among the six landslide related variables that were included in the landslide database used in the training phase. The landslide inventory map was constructed by interpreting aerial photographs, satellite images and field surveys and was separated into two datasets, one for training and one for validating the model. The outcomes of the validation process illustrated that the developed methodology efficiently provided the most susceptible areas and was in good agreement with the actual landslide locations. The area under the curve was estimated to be for the training and validating datasets 0.7575 and 0.7828 respectively. The produced landslide susceptibility map could be regarded from local and national authorities as a valuable mean to evaluate strategies or to prevent and mitigate the impact of landslides. Keywords: slope stability, fuzzy weighting, Shannon’s entropy index, Tzoumerka, Greece.

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  • Remote Sensing and GIS
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References
Aleotti, P. and Chowdhury, R., 1999. Landslide hazard assessment: summary review and new
perspectives, Bulletin of Engineering Geology and the Environment, 58, 21-44.
Aubouin, J., 1959. Contribution a l’ etude geologique de la Grece septentrionale: les confins de l’
Epire et de la Thessalie, Ann. Geol. Pays Hellen., 10, 1-483.
Bednarik, M., Magulova, B., Matys, M. and Marschalko, M., 2010. Landslide susceptibility
assessment of the Kralovany-Liptovsky Mikulas railway case study, Phys Chem Earth Parts
A/B/C, 35(3-5), 162-171.
Brunn, J.H., 1956. Contribution à l'étude géologique du Pinde septentrional et d'une partie de la
Macédoine occidentale, Annales géol. pays hellén.,1re série, 7, 358 pp., 20 pl.
Chung, C.J.F. and Fabbri, A.G., 2003. Validation of spatial prediction models for landslide hazard
mapping, Natural Hazards, 30(3), 451-472.
Conforti, M., Pascale, S., Robustelli, G. and Sdao, F., 2014. Evaluation of prediction capability of
the artificial neural networks for mapping landslide susceptibility in the Turbolo River
catchment northern Calabria, Italy, Catena, 113, 236-250.
Constantin, M., Bednarik, M., Jurchescu, MC. and Vlaicu, M., 2011. Landslide susceptibility
assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin
(Romania), Environ Earth Sci., 63, 397-406.
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R.
and Althuwaynee, O.F., 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, 65, 135-165.
Ermini, L., Catani, F. and Casagli, N., 2005. Artificial neural networks applied to landslide
susceptibility assessment, Geomorphology, 66, 327-343.
ESRI, 2013. ArcGIS Desktop: Release 10.1 Redlands, CA: Environmental Systems Research Institute.
Fawcett, T., 2006. An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874.
Feizizadeh, B. and Blaschke, T., 2013. GIS-multicriteria decision analysis for landslide
susceptibility mapping: comparing three methods for the Urmia lake basin, Iran, Natural
Hazards, 65(3), 2105-2128.
Ferentinou, M. and Sakellariou, M., 2007. Computational intelligence tools for the prediction of
slope performance, Computers and Geotechnics, 34, 362-384.
Hanley, J.A. and McNeil, B.J., 1982. The meaning and use of the area under a receiver operating
characteristic (ROC) curvek, Radiology, 143(1), 29-36.
IGME, 1961. Geological Map of Greece, Sheet Pramanda, 1:50.000, Athens, I.G.M.E. Publications.
Ilia, I. and Tsangaratos, P., 2015. Applying weight of evidence method and sensitivity analysis to
produce a landslide susceptibility map, Landslides, doi: 10.1007/s10346-015-0576-3.
Klir, G.J. and Yuan, B., 1995. Fuzzy sets and fuzzy logic: theory and applications, Prentice-Hall.
Korup, O. and Stolle, A., 2014. Landslide prediction from machine learning, Geology Today, 30(1),
-33.
Kouli, M., Loupasakis, C., Soupios, P., Rozos, D. and Vallianatos, F., 2014. Landslide susceptibility
mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island,
Greece, Environ Earth Sci., doi: 10.1007/s12665-014-3389-0.
Nefeslioglu, H.A., Sezer, E., Gokceoglu, C., Bozkir, A.S. and Duman, T.Y., 2010. Assessment of
landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey, Math
Probl Eng., doi: 10.1155/2010/901095, Article ID 901095.
Oh, H.J. and Pradhan, B., 2011. Application of a neuro-fuzzy model to landslide susceptibility
mapping in a tropical hilly area, Computers & Geosciences, 37(3), 1264-1276.
Pourghasemi, H.R, Moradi, H.R. and Fatemi Aghda, S.M., 2013a. Landslide susceptibility mapping
by binary logistic regression, analytical hierarchy process, and statistical index models and
assessment of their performances, Natural Hazards, 69(1), 749-779.
Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C. and Gokceoglu, C., 2013b. Landslide
susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran,
J. Earth Syst. Sci., 122(2), 349-369.
Pourghasemi, H.R., Mohammady, M. and Pradhan, B., 2012b. Landslide susceptibility mapping
using index of entropy and conditional probability models in GIS: Safarood Basin, Iran,
Catena, 97, 71-84. doi: 10.1016/j.catena.2012.05.005.
Pourghasemi, H.R., Pradhan, B. and Gokceoglu, C., 2012a. Application of fuzzy logic and analytical
hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran,
Natural Hazards, 63(2), 965-996.
Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector
machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers
& Geosciences, 51, 350-365.
Regmi, N.R., Giardino, J.R., McDonald, E.V. and Vitek, J.D., 2014. A comparison of logistic
regression based models of susceptibility to landslides in western Colorado, USA,
Landslides, 11, 247-262.
Regmi, N.R., Giardino, J.R. and Vitek, J.D., 2010a. Modeling susceptibility to landslides using the
weight of evidence approach: Western Colorado, USA, Geomorphology, 115, 172-187.
Regmi, N.R., Giardino, J.R. and Vitek, J.D., 2010b. Assessing susceptibility to landslides: Using
models to understand observed changes in slopes, Geomorphology, 122, 25-38.
Saito, H., Nakayama, D. and Matsuyama, H., 2009. Comparison of landslide susceptibility based on
a decision-tree model and actual landslide occurrence: the Akaishi mountains, Japan,
Geomorphology, 109(3-4), 108-121.
Shannon, C.E., 1948. A Mathematical Theory of Communication, Bell. Syst. Technol. J., 27, 379-
, SPSS, Inc. Released 2007. SPSS for Windows, Version 16.0. Chicago, SPSS Inc.
Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I. and Dick, O.B., 2012a. Spatial prediction of
landslide hazards in Vietnam: A comparative assessment of the efficacy of evidential belief
functions and fuzzy logic models, Catena, 96, 28-40.
Tien Bui, D., Pradhan, B., Lofman, O. and Revhaug, I., 2012c. Landslide Susceptibility Assessment
in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models,
Mathematical Problems in Engineering, Article ID 974638, 26 pp.,
doi:10.1155/2012/974638.
Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I. and Dick, O.B., 2012b. Landslide susceptibility
assessment in the Hoa Binh province of Vietnam using Artificial Neural Network,
Geomorphology, 171-172, 12-19.
Tsangaratos, P. and Benardos, A., 2014. Estimating landslide susceptibility through an artificial
neural network classifier, Nat. Hazards, 74(3), 1489-1516.
Tsangaratos, P. and Ilia, I., 2015. Landslide susceptibility mapping using a modified decision tree
classifier in the Xanthi Prefecture, Greece, Landslides, doi: 10.1007/s10346-015-0565-6.
Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A. and Hosseinali, F., 2010. A GIS-based neurofuzzy
procedure for integrating knowledgeand data in landslide susceptibility mapping,
Computers & Geosciences, 36(29), 1101-1114.
Yeon, Y.K., Han, J.G. and Ryu, K.H., 2010. Landslide susceptibility mapping in Injae, Korea, using
a decision tree, Engineering Geology, 16(3-4), 274-283.
Yilmaz, I., 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar,
Turkey: conditional probability, logistic regression, artificial neural networks, and support
vector machine, Environ. Earth Sci., 61, 821-836.
Youssef, A.M., Pourghasemi, H.R., El-Haddad, B.A. and Dhahry, B.K., 2015. Landslide
susceptibility maps using different probabilistic and bivariate statistical models and
comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia, Bull. Eng.
Geol. Environ., doi: 10.1007/s10064-015-0734-9.
Zhu, A-X., Wang, R., Qiao J., Qin, C-Z. Chen, Y., Liu, J., Du, F., Lin, Y. and Zhu, T., 2014. An
expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy
logic, Geomorphology, 214, 128-138.
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