ارزیابی روش‌های هیدرولوژیکی و داده‌کاوی در شبیه‌سازی و پیش‌بینی دبی جریان ماهانه

1.Adib, A., Salarijazi, M., and Najafpour, K. 2010a. Evaluation of Synthetic Outlet Runoff Assessment Models. J. Appl. Sci. Environ. Manage. 14: 3. 13-18.

2.Adib, A., Salarijazi, M., Shooshtari, M.M., and Akhonodali, A.M. 2011. Comparison between characteristics of geomorphoclimatic instantaneous unit hydrograph be produced by GcIUH based Clark Model and Clark IUH model. J. Mar. Sci. Technol. 19: 2. 201-209.

3.Adib, A., Salarijazi, M., Vaghefi, M., Mahmoodian-shooshtari, M., and Akhonali, A.M. 2010b. Comparison between GcIUH-Clark, GIUH-Nash, Clark-IUH, and Nash-IUH models. Turk. J. Engin. Environ. Sci. 34: 91-103.

4.Alikhanzadeh, A. 2007. Data mining, Edition 1, publishing of computer science, Babol. 344p. (In Persian)

5.Besaw, L.E., Rizzo, D.M., Bierman, P.R., and Hackett, W.R. 2010. Advances in ungauged stream flow prediction using artificial neural networks. Hydrology. 386: 27-37.

6.Crooke, B.F.W., Andrews, F., Spate, J., and Cuddy, S.M. 2005. IHACRES user guide. Technical Report 2005/19. Second Edition. iCAM, School of Resources, Environment and Society, The Australian National University, Canberra. http://www.toolkit.net.au/ihacres.

7.Croke, B.F.W., and Jakeman, A.J. 2008. Use of the IHACRES rainfall-runoff model in arid and semi-arid regions, P 41-48. In: H.S. Wheater, S. Sorooshian and K.D. Sharma (Eds.), Hydrological Modelling in Arid and Semi-arid Areas. Cambridge University Press, Cambridge.

8.Croke, B.F.W., and Jakeman, A.J. 2004. A catchment moisture deficit module for the IHACRES rainfall-runoff model. Environmental Modelling and Software. 19: 1-5.

9.El-Shafie, A., RedaTaha, M., and Noureldin, A. 2007. A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resource Manage. 21: 533-556.

10.Fallahi, M., Varvani, H., and Golian, S. 2012. Forecast precipitation using regression tree for flood control. 5^th conference of watershed and water resource management and land, Kerman. (In Persian)

11.Fathi, R. 2016. Spatial analysis of the hydrological drought. M.Sc. thesis, Gorgan University of Agricultural Sciences and Natural Resources, 99p. (In Persian)

12.Govindaraju, R.S. 2000. Artificial neural network in hydrology. I: Preliminary Concepts. J. Hydrol. Engin. 5: 2. 115-123.

13.Jain, A., and Kumar, A.M. 2007. Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comp. J. 7: 2. 585-592.

14.Jakeman, A.J., and Hornberger, G.M. 1993. How Much Complexity Is Warranted in a Rainfall-Runoff Model?. Water Resources Research. 29: 2637-2649.

15.Karamooz, M., and Araghinejad, Sh. 2005. Advanced Hydrology. Amirkabir University Press, 464p. (In Persian)

16.Kisi, O. 2005. Daily river flow forecasting using artificial neural networks and auto regressive models. Turk. J. Engin. Environ. Sci. 29: 9-20.

17.Littlewood, I.G., and Jakeman, A.J. 1994. A New Method of Rainfall-Runoff Modelling and its Applications in Catchment Hydrology. Environmental Modelling. 2: 142-171.

18.Lohani, A.K., Kumar, R., and Singh, R.D. 2012. Hydrological time series modeling: A comparison between adaptive neurofuzzy, neural network and autoregressive techniques. J. Hydrol. 442-443: 23-35.

19.McIntyre, N., and Al-Qurashi, A. 2009. Performance of ten rainfall-runoff models applied to an arid catchment in Oman. Environmental Modelling and Software. 24: 726-738.

20.Nabizadeh, M., Mosaedi, A., Hesam, M., and Dehghani, A.A. 2012. Comparing the performance of Fuzzy based models in stream flow on Lighvan River. J. Water Soil Cons. 19: 1. 117-134. (In Persian)

21.Quinlan, J.R. 1992. Learning with continuous classes. In: proceedings AI, 92 (Adams & Sterling, Eds), P 343-348. Singapore: World Scientific.

22.Salajegheh, A., Fathabadi, A., and Gholami, H. 2010. Predict river discharge using the nearest neighbor. 5th national conference on science and management engineering Iran. Gorgan University of Agricultural Sciences and Natural Resources. (In Persian)

23.Sorjamaa, A., Reyhani, N., and Lendasse, A. 2005. Input and structure selection for K-NN approximator. 8th International Conference on Artificial Neural Networks, Lecture Notes in Computer Science Springer, IWANN, Berlin, Pp: 958-992.

24.Taesombat, W., and Sriwongsitanon, N. 2010. Flood Investigation in the Upper Ping river basin using mathematical models. Kasetsart Natural Science. 44: 152-166.

25.Teimoori, F. 2014. Comparative study if meteorological indices with hydrological indices for drought monitoring using data mining method. M.Sc. thesis, Gorgan University of Agricultural Sciences and Natural Resources, 95p. (In Persian)

26.Two Crows Corporation. 1999. Introduction to data mining and knowledge discovery, third ed., Postmac, MD. Available at: www.twocrows.com, (April 29, 2000).

27.Yates, D., Gangopadhyay, S., Rajagopalan, B., and Strzepek, K. 2003. A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resources Research. 39: 7. 1114-1121.

28.Ye, W., Bates, B.C., Viney, N.R., Sivapalan, M., and Jakeman, A.J. 1997. Performance of Conceptual Rainfall-Runoff Models in Low-Yielding Ephemeral Catchments. Water Resources Research. 33: 1. 153-166.