1- Abdolahpour, M.R., and Satari, M.T., 2014. Prediction of Ahar River flow by using artificial neural networks and comparing it with fuzzy-neural network inference system. Journal of Water and Soil Conservation, 22(1) ,pp. 287-298 ( In Persian).
2- Ahmadi, F., Radmanesh, F.and Mirabasi Najafabadi, R., 2014. Comparing the performance of Support Vector Machines and Bayesian networks in predicting daily river flow (Case study: Baranduz Chai River), Journal of Water and Soil Conservation, 22(6), pp.171-186 ( In Persian).
3- Ahmadi, F., Dinpazhoh, Y. Fakherifard, A. and Darbandi, S., 2013. Comparing nonlinear time series models and genetic programming for daily river flow forecasting (Case study: Barandouz-Chai River), Journal of Water and Soil Conservation, 22(1), pp.151-169 ( In Persian).
4- Bohrani, A. and Fatehi, A., 2008. Application of Artificial Neural Network in Stream Flow Forecasting using Climatic Indices (Case Study: Nazloochay River Basin), Journal of Civil and Environmental Engineering University of Tabriz, 35(3),pp. 51-62.( In Persian).
5- Cain, J. 2001. Planning improvement in natural resource management. Centre for Ecology and Hydrology (CEH). Wallingford, UK.
6- Chiang, W. Hui-Chung, Y. 2014. Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy. Journal of Entropy. 16,pp. 4626-4647.
7- Davies, P. 2007. Conservation of Freshwater Ecosystem Values Project, Department of Primary Industries and Water Resources Division.
8- Dawson, C.W. Abrahart, R.J. Shamseldin, A.Y. Wibly, R.L. 2008. Flood estimation at ungauged sites uzingartifitial neural networks. Journal of Hydrology. 319,pp. 391-409.
9- Ghorbani, M.A. Ahmad Zadeh, H. Isazadeh, M. Terz, O. 2016a. A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction, Journal Environmental Earth Science. 75,,pp.465-476.
10- Ghorbani, Kh., Sohrabiyan, A. and Salari jezi, M., 2016b. Assessment of hydrological and data mining methods in simulating and predicting monthly flow flow (Case study: Arzakoush Hydrometry Station), Journal of Water and Soil Conservation, 23(1) ,pp. 203-217.( In Persian).
11- Ghorbani, M.A.,and Dehgani, R.,2015. Application of Bayesian Neural Networks, Support Vector Machines and Gene Expression Programming Analysis of Rainfall - Runoff Monthly (Case Study: Kakarza River). Journal of Irrigation Sciences and Engineering, 20. 39(2), pp.125-138.( In Persian).
12- Gizilbash, Z., Zakeriniya, M. Hezarjaribi, A. and Dehgani, A., 2014. Performance comparison of gene expression programming and artificial neural network methods to estimate water distribution uniformity in sprinkler irrigation, Journal of Water and Soil Conservation, 22(6) ,pp. 95-114.( In Persian).
13- Jalali, M., Pirniya, A. Soleimani, K. and Habibneghad roshan, M.,2015. Investigating the Function of Artificial Neural Network in River Flow Forecasting (Case Study: Ghareaghaj Basin of Fars Province). Journal of the Ecosystem of Desert Engineering,6(4), pp.15-26.( In Persian).
14- Kuikka S. and O., Varis 1997. Uncertainties of climate change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge, Journal of Boreal Environment Research. 2,pp.109-128.
15- Masoumi, F. and Kerachiyan, R., 2007. Optimal Design of Groundwater Groundwater Quality Monitoring Systems Using Discrete Entropy Theory Case Study: Tehran Aquife, Third National Congress of Civil Engineering, Shahid Beheshti,Tehran. ( In Persian).
16- Mohajerani, H., Mosaedi, A. Kholgi, M. and Meftah halgi, M., 2010. Introduction of business decision making networks and its application in water resource management, First National Conference on Coastal Water Resources Management.Sari, Mazandaran. ( In Persian).
17- Nikmanesh, M., 2014. Prediction of monthly average discharge using the hybrid model of artificial neural network and wavelet transforms (Case study: KorRiver-Pol-e-Khan Station), Journal of Water and Soil Conservation, 22(3) ,pp. 231-239.( In Persian).
18- Pollino, C. and Hart, B., 2006. Bayesian network models in natural resource management. Integrated Catchment Assessment and Management (ICAM) Centre of the Australian. National water commission.
19- Rajaee, T. and Ebrahimi, H., 2013. Application of neural network-wavelet model for prediction of non-stationary and non-linear characteristics of time series of groundwater level. Journal of Water and Soil Conservation,22(5), pp.99-115 ( In Persian).
20- Sadedin, A., Letcher, R. A., Jackeman, A. J. and Newham L. A., 2005. Bayesian decision network approach for assessing the ecological impact of salinity management. Journal of Mathematics and Computer in Simulation, 69, pp. 162-176.
21- Selgi, A., Radmanesh, F. and Soltani, K., 2014. Intelligent Modeling of the Monthly Period of Shur Ghorve River Basin with Artificial Neural Network. Journal of Water and Soil Conservation, 22(1), pp. 309-318.( In Persian).
22- Shannon, CE. 1948. A mathematical theory of communications, Journal of The Bell System Technical, 27,pp. 379-423.
23- Sheikhalipour, Z., Hasanpour, F. and Azimi, V.,2014. Comparison of artificial intelligence methods in estimation of suspended sediment load (Case Study: Sistan River), Journal of Water and Soil Conservation, 22(2) ,pp. 41-60.( In Persian).
24- Sonuga, J.O., 1976. Entropy principle applied to the rainfall-runoff process. Journal of Hydrology, 30(1-2), pp.81-94.
26- Wu, J. Li, P. and Qian, H. 2015. On the sensitivity of entropy weight to sample statistics in assessing water qualitystatistical analysis based on large stochastic samples. Journal of Environmental Earth Science. 74, pp.2185-2195.
27- Zhang JL, Ren J. 2011. The deﬁciencies and amendments of the calculation formulate of entropy and entropy weight in the theory of entropy. Journal of Statistics and Information Forum China Academic, 26(1), pp.1–5 (in Chinese).