1 |
Carrara, A. (1993) Potentials and pitfalls of GIS technology in assessing natural hazard. Geographical Information System in Assessing Natural Hazards, p. 128-137.
|
2 |
Choi, J,W., Oh, H,J., Won, J,S. and Lee, S. (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environment Earth Sciences, v.60, p.473-483.
DOI
ScienceOn
|
3 |
Choi, J.W., Oh, H.J., Lee, H.J., Lee, C.W. and Lee, S. (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, v.124, p.12-23.
DOI
ScienceOn
|
4 |
Dhakal, A.S., Amada, T. and Aniya, M. (2000) Landslide hazard mapping and its evaluation using GIS: an investigation of sampling schemes for a grid-cell based quantitative method. Engineering Remote Sensing, v.66, p.981-989.
|
5 |
Ercanoglu, M. and Gokceoglu, C. (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area(West Black Sea Region, Turkey). Engineering Geology, v.75, p.229-250.
DOI
ScienceOn
|
6 |
Fawcett, T. (2006) An introduction to ROC analysis. Pattern Recognition Letters, v.27, p.861-874.
DOI
ScienceOn
|
7 |
Kanungo, D.P., Arora, M.K., Sarkar, S. and Gupta, R.P. (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, v.85, p.347-366.
DOI
ScienceOn
|
8 |
Kanungo, D.P, Arora, M.K, Sarkar, S. and Gupta, R.P. (2009) A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, v.3, p.30-43.
DOI
|
9 |
Kim, K.S., Song, Y.S. and Cho, Y.C. (2006) Characteristics of rainfall and landslides according to the geological condition. The journal of Engineering Geology, v.16, p.201-214.
|
10 |
Lee, S., Choi, U., Chae, U. and Chang, B. (2002a) Landslide susceptibility analysis using weight of evidence. Geosciences and Remote Sensing Symposium
|
11 |
Lee, S., Choi, K. and Min, K. (2002b) Landslide susceptibility analysis and verification using the bayesian probability model. Environment Geology, v.43, p.120-131.
DOI
ScienceOn
|
12 |
Lee, S. (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology, v.52, p.615-623.
DOI
ScienceOn
|
13 |
Lee, S., Ryu, J,H., Lee, M,J. and Won, J,S. (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, v.44, p.820-833.
DOI
ScienceOn
|
14 |
Lee, S., Ryu, J,H., Won, J,S. and Park, H,J. (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, v.71, p.289-302.
DOI
ScienceOn
|
15 |
Pradhan, B. (2011a) Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modeling. Environmental and Ecological Statistics, v.18, p.471-493.
DOI
|
16 |
Oh, H.J. (2010) Landslide detection and landslide susceptibility mapping using aerial photos and artificial neural networks. Korean Journal of Remote Sensing, v.26, p.47-57.
|
17 |
Paola, J.D. and Schowengerdt, R.A. (1995) A review and analysis of back-propagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, v.16, p.3033-3058.
DOI
ScienceOn
|
18 |
Pradhan, B. (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, v.38, p.301-320.
DOI
|
19 |
Pradhan, B. (2011b) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three areas in Malaysia. Environmental Earth Sciences, v.63, p.329-349.
DOI
ScienceOn
|
20 |
Pradhan, B. and Lee, S. (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, v.60, p.1037-1054.
DOI
ScienceOn
|
21 |
Pradhan, B. and Lee, S. (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslide, v.7, p.13-30.
DOI
ScienceOn
|
22 |
Ross, T.J. (2004) Fuzzy logic with engineering applications, 2nd Edition, p.72-73.
|
23 |
Van Westen, C.J. and Terlien, M.T.J. (1996) An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surface Processes, v.21, p.853-868.
DOI
ScienceOn
|
24 |
Suzen, M.L. and Doyuran, V. (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Engineering Geology, v.71, p.303-321.
DOI
ScienceOn
|
25 |
Um, S.H., Lee, D.W. and Bak, B.S. (1964) 1:50,000 Geological Map of Pohang Sheet. Geological Survey of Korea
|
26 |
Vahidnia, M., Alesheikh, A., Alimohammadi, A. and Hosseinali, F. (2009) Design and development of an intelligent extension for mapping landslide susceptibility using artificial neural network. Computational Science and Its Application, v.5592, p.17-32.
|
27 |
Wang, H.B. and Sassa, K. (2005) Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology, v.47, p.956-966.
DOI
|
28 |
Yilmaz, I. (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environment Earth Sciences, v.61, p.821-836.
DOI
|
29 |
Zadeh, L.A. (1965) Fuzzy sets. Information and Control, v.8, p.338-353.
DOI
|