Referências bibliográficas

• [1] Akgun, A., Dag, S., Bulut, F., Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models?. Environmental Geology. 54, 1127-1143, 2008.

• [2] Akgun, A., Türk, N., Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environmental Earth Sciences?. 61, 595-611, 2010

• [3] Arora, M.K., Das Gupta, A.S., Gupta, R.P., ?An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas?. International Journal of Remote Sensing, 25, 559–572, 2004.

• [4] Champati ray, P. K., Dimri, S., Lakhera, R. C. and Sati, S., 2007, Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya, Landslides, 4, 101-111.

• [5] Chauhan, S., Arora, M. K., and Gupta, N. K., 2010a, Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network, International Journal of Applied Earth Observation and Geo Information, 12, 340-350.

• [6] Chauhan, S., Sharma, M and Arora, M. K., 2010b, Landslide Susceptibility Zonation of Chamoli region, Garhwal Himalayas,using Logistic Regression Model, Landslides, Published Online.

• [7] Chi, K.-H., Park, N.-W., Chung, C.-J., 2002b. Fuzzy logic integration for landslide hazard mapping using spatial data from Boeun, Korea. Proc. of Symp. on Geospatial Theory, Processing and Applications, Ottawa.

• [8] Dai, F.C., Lee, C.F., Li, J., Xu, Z.W., 2001. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40, 381– 391.

• [9] Gupta, V., Sah, M.P., Virdi, N.S., Bartarya, S.K., 1993. Landslide Hazard Zonation in the Upper Satlej Valley, District Kinnaur, Himachal Pradesh. Journal of Himalayan Geology, 4, 81–93.

• [10] Kanungo DP, Arora MK, Sarkar S, Gupta RP (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, 85, 347–366

• [11] 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, 44, 820-833.

• [12] Mathew J., Jha V. K., Rawat G. S., 2007. Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Science, 92, 628–638.

• [13] Mathew, J., Jha, V.K. and Rawat, G.S., 2009. Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides, 6, 17-26.

• [14] Nefeslioglu, H.A., Sezer, E., Gokceoglu, C., Bozkir, A.S., Duman, T.Y., 2010. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey. Mathematical Problems in Engineering.

• [15] Olden, J.D., Joy, M.K., Death, R.G., 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modeling, 178, 389–397.

• [16] Pareek, N., Sharma, M. L., Arora, M. K., 2010, Impact of seismic factors on Landslide Hazard Zonation: A case study in part of Indian Himalayas, Landslides, 7, 191-201.

• [17] Pradhan, B. and Lee, S., 2009. Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Applied Geomatics, 1, 3–15.

• [18] Pradhan, B., 2010. Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems 3, 370-381.

• [19] Pradhan, B., 2011. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Sciences 63, 329-349.

• [20] Pradhan, B., Lee, S., 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling Andamp; Software 25, 747-759.

• [21] Pradhan, B., Lee, S., Buchroithner, M.F., 2010a. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers Environment and Urban Systems 34, 216-235.

• [22] Pradhan, B., Sezer, E.A., Gokceoglu, C., Buchroithner, M.F., 2010b. Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia). IEEE Transactions on Geoscience and Remote Sensing 48, 4164-4177.

• [23] Saha, A.K., Gupta, R.P., Arora, M.K., 2002. GIS-based landslide hazard zonation in a part of the Himalayas. International Journal of Remote Sensing, 23, 357–369.

• [24] Saha, A.K., Gupta, R.P., Sarkar, I., Arora, M.K., Csaplovics, E., 2005. An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2, 61–69.

• [25] Sezer, E.A., Pradhan, B., Gokceoglu, C., 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 38, 8208-8219.

• [26] Valdiya, K.S. 1980. Geology of Kumaun Lesser Himalaya. Wadia Institute of Himalayan Geology, Dehra Dun. 292p.

• [27] Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers G., Verstraeten, G. and Vandekerckhove, L., 2006. Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology, 76, 392-410.