1. Cai, X., Y. Li, X. Guo and W. Wu. 2014 .Mathematical model for flood routing based on cellular automaton. Water Science and Engineering, 7(2): 133-142.
2. Dawson, C.W., R.J. Abrahart, A.Y. Shamseldin and R.L. Wilby. 2006. Flood Estimation at ungauged sites using artificial neural networks. Journal of Hydrology, (319): 391-409. [
DOI:10.1016/j.jhydrol.2005.07.032]
3. Douvinet, J., D. Delahaye and P. Langlois. 2007. Use of cellular automata in physical geography, 15th European Colloquium of Theoretical and Quantitative Geography, Montreux, Switzerland.
4. Dalal, U., M. Fathi and K. Khoshdel. 2017. Application of new multi-criteria decision-making methods for the estimation of flooding potential with emphasis on geomorphic factors (Case Study: Ajorlu Basin), Geographic Space, 17(59): 67-82 (In Persian).
5. Elsafi, H. 2014. Artificial Neural Networks (ANNs) for floodforecasting at Dongola Station in the River Nile, Sudan,Alexandria Engineering Journal, 53(3): 655-662. [
DOI:10.1016/j.aej.2014.06.010]
6. Gomez, H. and T. Kavzoglu. 2005. Assessment of shallow landslidesusceptibility using artificial neural networks in Jabonosa RiverBasin. Venezuela. Eng Geol, 78(1-2): 11-27. [
DOI:10.1016/j.enggeo.2004.10.004]
7. Gholizadeh, A., E. Ghanavati. H. Afsharmanesh and H. Amanullahpour. 2018. Fuzzy Model Efficiency on Flooding Potential in the Basin of Zangmar, Geographic Space, 17(60): 227-245 (In Persian).
8. Haykin, S. 1999. Neural networks: a comprehensive foundation, 2ndend. Prentice Hall, New Jersey.
9. Hong, H., P. Tsangaratos, I. Ilia, J. Liu, A. Zhu and W. Chen. 2018. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China, Science of the Total Environment, (625): 575-588. [
DOI:10.1016/j.scitotenv.2017.12.256]
10. Kasiviswanathana, K.S., H. Jianxun, K.P. Sudheerb and T. Joo-Hwa. 2016. Potential application of wavelet neural network ensembleto forecast stream flow for flood management, Journal of Hydrology, 536(4): 161-173. [
DOI:10.1016/j.jhydrol.2016.02.044]
11. Kerh, T. and C.S. Lee. 2006. Neural networks forecasting of flood discharge atan unmeasured station using river upstream information. Journal of Advances in Engineering Software, (37): 533-543. [
DOI:10.1016/j.advengsoft.2005.11.002]
12. Kholghi, M. 2002. The Use of MDCM Method in Prioritizing Sub-Watersheds structural Flood Control. Iranian Journal of Natural Resources, (55): 479-490 (In Persian).
13. Lek, S., M. Delacoste, P. Baran, I. Demopoulos, J. Lauga and S. Aulanier. 1996. Application of neural networks to modelling non-linearrelationships in ecology. Ecol Model, (90): 39-52. [
DOI:10.1016/0304-3800(95)00142-5]
14. Liang, S. and C.R.C. Mohanty. 1997. Optimization of GIS-Based Flood Hazard Zoning a Case Study at the Mahanady Command Area in Cuttack District, Orrisa, India. Journal of Chinese Soil and Water Conservation, (28): 11-20.
15. Maier, H.R. and G.C. Dandy. 1996.The use of artificial neural networks forthe prediction of water quality parameters. Water Resour Res, (4): 1013-1022. [
DOI:10.1029/96WR03529]
16. Reza, M.P. and S. Touraj. 2007. Flood zoning using GIS system (study plan: part of Qara Aghaj river in Fars province), the first urban GIS conference (In Persian).
17. Alireza, M. and M. Habibnejad Roshan. 2006. Study of flood prediction systems in Iran and their role in the protection of lands and residential areas along the canals, the first national conference on canal engineering, (In Pensian).
18. Hussein, N., M.A. Moghadam and M. Aramesh. 2013. Application of artificial neural network in simulation and flood prediction in Sarbaz catchment, Quarterly Journal of Geography and Development, 11(31): 15-28.
19. Nabizadeh, M., A. Masaedi. Hesam and A. Dehghani. 2012. Comparison of the performance of models based on fuzzy logic in predicting the daily discharge of Liquan River. Soil and Water Conservation Research, (19): 117-134 (In Persian).
20. Pradhan, B. 2009. Groundwater potential zonation for basalticwatersheds using satellite remote sensing data and GIS techniques.Central Eur Journal Geosci, 1(1): 120-129 [
DOI:10.2478/v10085-009-0008-5]
21. Pereira, F.A.J. and S. Dos. 2006. Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data. Journal of Hydrology, (317): 31-48. [
DOI:10.1016/j.jhydrol.2005.05.007]
22. Qanavati, E., O. Karam and M. Alikhani. 2012. Flood Risk Assessment and Zoning in Farahzad Basin (Tehran) Using Fuzzy Model, Journal of Geography and Environmental Planning, 23rd Consecutive Year, (48): 38 -121 (In Persian).
23. Sahoo, G.B., C. Ray and D. Carlo. 2006. Use of neural network topredict flash flood and attendant water qualities of a mountainous stream onOahu, Hawaii. Journal of Hydrology, (327): 525-538. [
DOI:10.1016/j.jhydrol.2005.11.059]
24. Tokar, A.S. and P.A. Johnson. 1999. Rainfall-runoff modeling using Artificial Neural Network. Journal of Hydrology Engineering, (4): 232-239. [
DOI:10.1061/(ASCE)1084-0699(1999)4:3(232)]
25. Thirumalaiah, K. and M.C. Deo. 2000. Hydrological forecasting using neural network. Journal of Hydrology Engineering, (5): 180-189. [
DOI:10.1061/(ASCE)1084-0699(2000)5:2(180)]
26. United Nations Environment Program. 2002. Early warning, forecasting. And operational flood risk monitoring in Asia (Bangladesh,China and India). http://www.unep.org/geo/geo3.asp.Accessed 21 Aug 2010.
27. Xiao, Y., Sh. Yi and T. Zh. 2017. Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference, Science of the Total Environment, (600): 1034-1046. [
DOI:10.1016/j.scitotenv.2017.04.218]
28. Vali, A., M. Ramesht, A. Seif and R. Ghazavi. 2010. Comparing efficiency neural networks and regression models for prediction of watershed sediment load flow forecasting by artificial neural network and conventional model. Alexandria Engineering Journal, (50): 345-350. [
DOI:10.1016/j.aej.2012.01.005]
29. Yamani, M. and M. Enayati. 2006. The analyses of flood data in relation to thegeomorphologicspecification of Fas hand and Behjatabad basins. 2005. Journal of Geography Research, (54): 47-57 (In Persian). [
DOI:10.2747/1538-7216.47.1.54]
30. Rezai, A., M. Mahdavi, K. Lox, S. Feyznaya and M.H. Mehdian. 2007. Regional peak flows modelling of Sefid Rood Dam's sub basins using artificialneural network. Journal of Science and Technology of Agriculture and Natural Resources, (11): 25-39 (In Persian).