مدل سازی بارش – رواناب روزانه رودخانه دره رود واقع در استان اردبیل

1-    Aytek, A. and Alp, M., 2008. An application of artificial intelligence for rainfall-runoff modeling. Journal of Earth System Science, 117(2), pp.145-155.

2-    Behzad, M. Asghari, K. Eazi, M. and Palhang, M., 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36(4), pp.7624-7629.

3-    Botsis, D. Latinopoulos, P. and Diamantaras, K., 2011. Rainfall-Runoff Moeling Using Suport Vector Regression and Artificial Neural Networks. In 12th International Conference on Environmental Science and Technology, Rhodes, Greece.

4-    Ferreira, C., 2006. Automatically defined functions in gene expression programming. In Genetic Systems Programming. Springer Berlin Heidelberg.

5-    Ferreira, C., 2001. Gene Expression Programming: a new adaptive algorithm for solving problems. Complex Systems, 13(2), pp.87–129.

6-    Ghorbani, M.A. and Dehghani, R., 2015. Application of bayesian neural networks, support vector machines and gene expression programming analysis of rainfall - runoff monthly (case study: Kakarza river). Irrigation Sciences and Engineering, 39(2), pp.125–138. (In Persian).

7-    He, Z. Wen, X. Liu, H. and Du, J., 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509, pp.379-386.

8-    Jayawardena, A.W. Muttil, N. and Fernando, T.M.K.G., 2005. Rainfall-runoff modeling using genetic programming. In International Congress on Modeling and Simulation Society of Australia and New Zealand.

9-    Kisi, O., 2008. Stream flow forecasting using neuro‐wavelet technique. Journal of Hydrological Processes, 22(20), pp.4142-4152.

10- Marofi, S. Amir-Moradi, K. and Parsafar, N., 2012. River flow prediction using Artificial Neural Network and Wavelet Neural Network models (Case study: Barandozchay River). Water and Soil Science, 23(3), pp.93–103. (In Persian).

11- Nabizadeh, M. Mosaedi, A. and Dehghani, A.A., 2012. Intelligent estimation of stream flow by Adaptive Neuro-Fuzzy Inference System). Water and Irrigation Management, 2(1), pp.69–80. (In Persian).

12- Nourani, V. Komasi, M. and Mano, A., 2009. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Journal of Water Resources Management, 23(14), pp.2877-2894.

13- Rezaei, E. Khashei-Siuki, A. and Shahidi A., 2014. Design of groundwater level monitoring network, using the model of least squares support vector machine (LS-SVM). Iranian Journal of Soil and Water Research, 45(4), pp.389-396. (In Persian).

14- Salajegheh, A. Fathabadi, A. and Mahdavi, M., 2009. Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process. Journal of Range and Watershed Management, 62(1), pp.65-79.

15- Shafiee-Jood, M. Abrishamchi, A. and Salavitabar, A., 2012. Evaluation of water resources development plans in multi-reservoir darehrood sub catchment system using performance indices. Journal of Water and Wastewater, 3, pp.22-32. (In Persian).

16- Shu, C. and Ouarda, T.B.M.J., 2008. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology, 349(1), pp.31-43.

17- Soltani, A. Ghorbani, M.A. Fakheri-Fard, A. Darbandi, S. and Farsadizadeh, D., 2010. Genetic programming andi application in rainfall-runoff modeling. Water and Soil Science, 20(4), pp.62–71. (In Persian).

18- Suykens, J.A. De Brabanter, J. Lukas, L. and Vandewalle, J., 2002. Weighted least squares support vector machines, pp. robustness and sparse approximation. Neurocomputing, 48(1), pp.85-105.

19- Taheri, H. and Ghafouri M., 2012. Comparison between active learning method and support vector machine for runoff modeling. Journal of Hydrology and Hydromechanics, 60(1), pp.16-32.

20- Tokar, A.S. and Markus, M., 2000. Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering, 5(2), pp.156-161.

21- Vapnic, V.N., 1998. Statistical Learning Theory. Wiley, New York, USA.

22- Yu, P.S. Chen, S.T. and Chang, I.F., 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3), pp.704-716.