پهنه بندی مناطق مستعد به زمین لغزش با استفاده از سیستم استنتاجی فازی عصبی(ANFIS)(مطالعه موردی: حوضه رودخانه سنگورچای)

پوستی زاده، ندا، نجفی، نعیمه (1390). مقایسه کاربرد شبکه عصبی مصنوعی (ANN) با سیستم استنتاج فازی (FIS) در پیش بینی جریان رودخانه زاینده رود. تحقیقات منابع آب ایران، سال 7، شماره 2، صص 97- 92.

دهقانی، نوید، وفاخواه، مهدی، بهره مند، عبدالرضا (1395). مدلسازی بارش رواناب با استفاده از شبکه عصبی مصنوعی و شبکه فازی عصبی تطبیقی در حوزه آبخیز کسیلیان. پژوهشنامه مدیریت حوزه آبخیز، سال 7، شماره 13، صص 137- 128.

رستمی، مهناز، پهلوانروی، احمد، مقدم نیا، علیرضا (1394). پیش بینی خشکسالی با بکارگیری از مدل های شبکه عصبی مصنوعی و سیستم استنتاج عصبی فازی تطبیقی در حوزه مند استان فارس، مجله مخاطرات محیط طبیعی، سال 4، شماره 6، صص 32- 21.

غفاری، غلامعلی، وفاخواه، مهدی (1392). شبیه سازی فرایند بارش رواناب با استفاده از شبکه عصبی مصنوعی و سیتم فازی عصبی تطبیقی (مطالعه موردی: حوزه آبخیز حاجی قوشان). پژوهشنامه مدیریت حوزه آبخیز، سال 4، شماره 8، صص 136- 120.

نبی زاده، مرتضی، مساعدی، ابوالفضل، حسام، موسی، دهقانی، امیراحمد، ذاکری نیا، مهدی، مفتاح هلقی، مهدی (1390). پیش بینی جریان رودخانه با استفاده از سامانه استنتاج فازی و سامانه استنتاج فازی عصبی تطبیقی (ANFIS)، علوم و مهندسی آبخیزداری ایران، سال 5، شماره 17، صص 14- 7.

Akgun, A., Bulut, F (2007). GIS- based landslide susceptibility for Arsin-Yomra region. Environment Geology, Volume 51, pp 1377-1387.

Alexander, I., Morton, H (1990). An Introduction to Neural Computing, Chapman and Hall, London. 468 pp.

Anbalagan, R (1992). Landslide susceptibility evaluation and zonation mapping in mountainous terrain. Eng. Geol. Volume 32, pp 269–27.

Bhattacharya, B., price, R., Solomatine, D. (2005). Data- driven modeling in the context of sediment transport. Phys Chem. Earth. volume 30, pp 297-302.

Ayalew, L., Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology volume 65, pp15–31.

Choi, j., Oh, H., Lee, H., Lee, C., 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, Volume 124, PP 12-23.

Cubito, A., Ferrara, V., Pappalardo, G. (2005). Landslide hazard in the Nebrodi Mountains (Northeastern Sicily). Geomorphology. volume 66, pp 359–372.

Dahal, R. K., Hasegawa, S., Nonomura, S., Yamanaka, M., Masuda, T., Nishino, K. (2008). GIS-based weight of evidence modeling of rainfall- induced landslides in small catchment for landslide susceptibility mapping. Environmental Geology, Volume 54, pp 311-324.

Ermini, L., Catani, F., Casagli, N., (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, Volume 66, pp327–343.

Fernandes, N.F., Guimarães, R.F., Gomes, R.A.T., Vieira, B.C., Montgomery, D.R., Greenberg, H. (2004). Topographic controls of landslides in Rio de Janeiro: filed evidence and modeling. Catena. volume 55, pp 163–181.

Gokceoglu, C., Sonmez, H., Nefeslioglu, H.A., Duman, T.Y., Can, T. (2005). The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide susceptibility map of its near vicinity. Engineering Geology, Volume 81, pp 65–83.

Gomez, H., Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng. Geol. Volume 78, pp 11–27.

Guyon, I., Wang, P.S.P. (1993). Advances in Pattern Recognition Systems Using Neural Network Technologies.World Scientific, Singapore, New Jersey, London, Hong Kong.451 pp.

Haykin, S. (1994). Neural Networks. Macmillan College Publishing Company, New York. 438 pp.

Iwahashi, J., Watanabe, S., Furuya, T. (2003). Mean slope–angle frequency distribution and size frequency distribution of landslide masses in Higashikubiki area, Japan.Geomorphology. volume 50, pp 349–364.

Jang, J. (1993). ANFIS: Adaptive- network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. volume 23, pp665-685.

Kawabata, D., bandibas, J. (2009). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial neural network (ANN). Geomorphology, Volume 113, PP 97- 109.

Kisi, O. (2005). Suspended sediment estimation using neuro- fuzzy and neural network approaches. Hydrological Sciences Journal. volume 50, issue 4, pp683-696.

Lee, S., Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, volume  40, pp1095–1113.

Lee, S., Ryu, J. Min, K. Won, J. (2003). Landslide susceptibility analysis using GIS and artificial neural networks. Earth Surface Processes and Landforms, Volume 28, pp1361–1376.

Lu, P., Rosenbaum, M.S. (2003). Artificial neural network and gray system for the prediction of slope stability. Natural Hazards. volume  30,pp 383–398.

Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A. (2006). Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology. volume 94,pp 379–400.

Moreiras, S.M. (2005). Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina. Geomorphology. volume 66,pp 345–357.

Nigrin, A. (1993). Neural Network for Pattern Recognition.The MIT Press, Cambridge, England.

Pradhan, B., Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis back propagation artificial neural network and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental modeling& Software, Volume 25, PP 747- 759.

Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning internal representations by error propagation. In: Rumelhart, D.E., McLelland, J.L., The PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume.1. InMIT Press, Cambridge, Massachusetts, pp 318–362.

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 Application, Volume 38, PP 8208- 8219.

Sethi, I.K., Jain, A.K. (1991). Artificial Neural Networks and Statistical Pattern Recognition.North-Holland, London, New York, Tokyo.

Takagi, T., Sugeno, M. (1983). Derivation of fuzzy control rules from human operator control actions. In Proceedings of the IFAC Symposium on fuzzy information, knowledge representation and decision analysis, July. volume 1983, pp 55-60.

Wang, D., Pu, R., Gong, P., Yang, R. (1995). Predicting forest yield with an artificial neural network and multiple regression.Chinese University of Hong Kong, Hong Kong, pp.771–780.

Wang, H. B., Sassa, K. (2005). Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology, Volume 47, pp 956-966.

Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat- Turkey). Computers& Geosciences, Volume 35, PP 1125- 1138.

Zezere, J., Ferreira, A., Rodrigues, M. (1999). The role of conditioning and triggering factors in the occurrence of landslides: a case study in the area north of Lisbon (Portugal). Geomorphology. volume 30,pp 133–146.