1. Banejad, H., M . Kamali, K . Amirmoradi and E. Olyaie. 2014. Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model) Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah). Iranian Journal of Health and Environment, 6(3): 277-294 (In Persian).
2. Dastorani, M.T., K.H. AzimiFashi and A. Talebi. 2011. Estimation of Suspended Sediment Using Journal of Artificial Neural Network. Watershed Management Research, 3(6): 61-74 (In Persian).
3. Dayhoff, J.E. 1990. Neural Network Principles: New York: Prentice-Hall International,650pp.
4. Eshghi, P., J. Farzad Mehr, M.T. Dastorani and Z. Arab Asadi.2017. The Effectiveness of Intelligent Models in Estimating the River Suspended Sediments (Case Study: Babaaman Basin, Northern Khorasan). Watershed Management Research, 7(14) :88-95(In Persian).
5. Faryadi, S., K. Shahedi and M. Nabatpoor. 2013. Investigation of Water Quality Parameters in Tadjan River using Multivariate Statistical Techniques. Journal of Watershed Management, 6: 75-92 (In Persian) .
6. Gazzaz, N.M., M.K. Yusoff, A. Zaharin Aris, H. Juahir and M.F. Ramli. 2012. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Journal of Marine Pollution Bulletin, 64(1): 2409-2420.
7. Ghorbani, M.A. and R. Dehghani. 2017. Comparison of Bayesianneural Network, Artificial Neural Network Gene Expression Programming in River Water Quality (Case Study: Belkhviachay river). Watershed Management Research,8(15): 13-24 (In Persian).
8. Khanna, T.1990. Foundation of neural networks: Addison-Wesley Series in New Horizons in Technology: New York: Addison-Wesley,540pp.
9. Kaveh, A. and A. Iran Manesh. 2005. Artificial neural network in the optimization of structures: Building and Housing Research Center,112-120pp (In Persian)
10. Maier, H.R. and G.C. Dandy. 1996. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research, 32(4): 1013-.1022.
11. Najah, A., A. Elshafie, O. Karim and O. Jaffar.2009. Prediction of Johor river water quality parameters using artificial neural networks. European Journal of scientific research, 28(4): 422-435.
12. Safavi, A.A. and J.A. Romania.1997. Application of wavelet-based neural networks to modelling and optimisation of an experimental distillation column. (IFAC Journal of) Engineering Applications of Artificial Intelligence,10(3): 301-313.
13. Saf Sheken, F., N. Pir Moradian and R. Sharifian. 2012. Simulation of rainfall-runoff hydrograph and the use of artificial neural network model of rainfall in the catchment kasilian. Iran-Watershed Management Science & Engineering, 5(15): 1-10 (In Persian)
14. Sayadi, H., A. Olad Ghafari, A. Faalian and A. A. Sadr aldini. 2009. Comparison of RBF and MLP Neural Networks Performance for Estimation of Reference Crop Evapotranspiration. Soil and Water, 19(2): 1-12 (In Persian)
15. Singh, K.P., A. Basant, A. Malik and G. Jain .2009. Artificial neural network modeling of the river water quality-A Case Study. Journal of Ecological Modeling, 220(2): 888-895.
16. Taklifi , A and G. Asadolah Fardi .2009. Comparison two models RNN and mlp for predict the amount of salt in AjiChay. Tenth Seminar irrigation and reduce evaporation,90-102pp (in Persian)
17. Tokar, A. S and P. A. Johnson . 1999 . Rainfall- Runoff modeling using artificial neural networks. Journal of Hydrology Engineering, 3(1): 232-239.
18. Wang, D., A.A. Safavi and J.A. Romagnoli .2000 . Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal, 46(8): 1607-1615.
19. Yar Mohammadi, A., M. Chit Sazan, K. Rangzan and J. Mozafari Zadeh. 2006. Using artificial neural networks for simulate the water quality of river floodplains. First Conference on Environmental Engineering, 68-75pp (In Persian).
20. Zhu, Y.M., X.X. Lu and Y. Zhou . 2007. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjian River in the Upper Yangtze Catchment. Geomorphology, 84(1): 111-125.
21. Banejad, H., M . Kamali, K . Amirmoradi and E. Olyaie. 2014. Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model )Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah). Iranian Journal of Health and Environment, 6(3): 277-294 (In Persian).
22. Dastorani, M.T., K.H. AzimiFashi and A. Talebi. 2011. Estimation of Suspended Sediment Using Journal of Artificial Neural Network. Watershed Management Research, 3(6): 61-74 (In Persian).
23. Dayhoff, J.E. 1990. Neural Network Principles: New York: Prentice-Hall International,650pp.
24. Eshghi, P., J. Farzad Mehr, M.T. Dastorani and Z. Arab Asadi.2017. The Effectiveness of Intelligent Models in Estimating the River Suspended Sediments (Case Study: Babaaman Basin, Northern Khorasan). Watershed Management Research, 7(14) :88-95(In Persian). [
DOI:10.29252/jwmr.7.14.95]
25. Faryadi, S., K. Shahedi and M. Nabatpoor. 2013. Investigation of Water Quality Parameters in Tadjan River using Multivariate Statistical Techniques. Journal of Watershed Management, 6: 75-92 ( In Persian ).
26. Gazzaz, N.M., M.K. Yusoff, A. Zaharin Aris, H. Juahir and M.F. Ramli. 2012. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Journal of Marine Pollution Bulletin, 64(1): 2409-2420. [
DOI:10.1016/j.marpolbul.2012.08.005]
27. Ghorbani, M.A. and R. Dehghani. 2017. Comparison of Bayesianneural Network, Artificial Neural Network Gene Expression Programming in River Water Quality (Case Study: Belkhviachay river). Watershed Management Research,8(15): 13-24 (In Persian).
28. Khanna, T.1990. Foundation of neural networks: Addison-Wesley Series in New Horizons in Technology: New York: Addison-Wesley,540pp.
29. Kaveh, A. and A. Iran Manesh. 2005. Artificial neural network in the optimization of structures: Building and Housing Research Center,112-120pp (In Persian)
30. Maier, H.R. and G.C. Dandy. 1996. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research, 32(4): 1013-.1022. [
DOI:10.1029/96WR03529]
31. Najah, A., A. Elshafie, O. Karim and O. Jaffar.2009. Prediction of Johor river water quality parameters using artificial neural networks. European Journal of scientific research, 28(4): 422-435.
32. Safavi, A.A. and J.A. Romania.1997. Application of wavelet-based neural networks to modelling and optimisation of an experimental distillation column. (IFAC Journal of) Engineering Applications of Artificial Intelligence,10(3): 301-313. [
DOI:10.1016/S0952-1976(97)00009-2]
33. Saf Sheken, F., N. Pir Moradian and R. Sharifian. 2012. Simulation of rainfall-runoff hydrograph and the use of artificial neural network model of rainfall in the catchment kasilian. Iran-Watershed Management Science & Engineering, 5(15): 1-10 (In Persian)
34. Sayadi, H., A. Olad Ghafari, A. Faalian and A. A. Sadr aldini. 2009. Comparison of RBF and MLP Neural Networks Performance for Estimation of Reference Crop Evapotranspiration. Soil and Water, 19(2): 1-12 (In Persian)
35. Singh, K.P., A. Basant, A. Malik and G. Jain .2009. Artificial neural network modeling of the river water quality-A Case Study. Journal of Ecological Modeling, 220(2): 888-895. [
DOI:10.1016/j.ecolmodel.2009.01.004]
36. Taklifi , A and G. Asadolah Fardi .2009. Comparison two models RNN and mlp for predict the amount of salt in AjiChay. Tenth Seminar irrigation and reduce evaporation,90-102pp (in Persian)
37. Tokar, A. S and P. A. Johnson . 1999 . Rainfall- Runoff modeling using artificial neural networks. Journal of Hydrology Engineering, 3(1): 232-239. [
DOI:10.1061/(ASCE)1084-0699(1999)4:3(232)]
38. Wang, D., A.A. Safavi and J.A. Romagnoli .2000 . Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal, 46(8): 1607-1615. [
DOI:10.1002/aic.690460812]
39. Yar Mohammadi, A., M. Chit Sazan, K. Rangzan and J. Mozafari Zadeh. 2006. Using artificial neural networks for simulate the water quality of river floodplains. First Conference on Environmental Engineering, 68-75pp (In Persian).
40. Zhu, Y.M., X.X. Lu and Y. Zhou . 2007. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjian River in the Upper Yangtze Catchment. Geomorphology, 84(1): 111-125. [
DOI:10.1016/j.geomorph.2006.07.010]