ارزیابی عملکرد توابع کرنل در تخمین جریان رودخانه‌ها با استفاده از ماشین بردار پشتیبان

1.Adamowski, J. 2013. Using support vector regression to predict direct runoff, base flow
and total flow in a mountainous watershed with limited data in Uttaranchal, India. Versita J. 45: 1. 71-83.

2.Adeli, A., Fathi-Moghadam, M., and Musavi Jahromi, H. 2014. Using Stochastic Models to Produce Artificial Time Series and Inflow Prediction: A Case Study of Talog Dam reservoir, Khuzestan Province, Iran. J. Inter. Bull. Water Resour. Dev. 2: 5. 1-13. (In Persian)

3.Asefa, T., Kemblowski, M., McKee, M., and Khalil, A. 2005. Multi-time scale stream flow predictions: The Support vector machines approach. J. Hydrol. 318: 1-4. 7-16.

4.Azani, A., Fazelifard, M.H., and Ghorbani, M.A. 2014. Simulation of Urmia Lake water level using support vector machines and artificial neural network. The 13^th Conference of the hydraulic Iran, Tabriz University. (In Persian) 

5.Baofeng, G., Gunn, S.R., Damper, R.I., and Nelson, J.D.B. 2008. Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification. IEEE Transactions on Image Processing. 17: 4. 622-629.

6.Bani Habib, M.A., and Valipour, M. 2008. Comparative assessment of ARMA, ARIMA and autocorrelated artificial neural network models in forecasting inflow to the Dez reservoir. First International Conference on Water Crisis, Zabul University. (In Persian)

7.Basak, D., Pal, S., and Patranabis, D.C. 2007. Support vector regression. Neural Inf. Process. 11: 203-225.

8.Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. 1994. Time Series Analysis: Forecasting and Control. Third edition, Prentice Hall. 598p.

9.Damle, C., and Yalcin, A. 2007. Flood prediction using time series data mining. J. Hydrol. 333: 2-4. 305-316.

10.Dibike, Y., Velickov, S., Solomatine, D., and Abbott, M. 2001. Model induction with of support vector machines: Introduction and applications. J. Comp. Civil Engin. 15: 3. 208-216.

11.Eskandari, A., Nouri, R., Meraji, H., and Kiaghadi, A. 2012. Development of appropriate model based on artificial neural network and support vector machine for forecasting 5-Days Biochemical Oxygen Demand (BOD5). J. Ecol. 61: 71-82. (In Persian)

12.Fletcher, R. 1987. Practical Methods of Optimization. Wiley, New York. 456p.

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

14.Jian, Y., Cheng, C.T., and Chau, K.W. 2006. Using support vector machines for long-term discharge prediction. J. Hydrol. Sci. – des Sci. Hydrol. 51: 4. 599-612.

15.Kavzoglu, T., and Colkesen, I. 2009. A kernel functions analysis for support vector machines for land cover classification. Inter. J. Appl. Earth Obs. Inf. 11: 5. 352-359.

16.Kakaei Lafadani, E., Moghaddam Nia, A., Ahmadi, A., Jajarmizadeh, M., and Ghafari, M. 2013. Stream flow simulation using SVM, ANFIS and NAM models (A Case study). Caspian J. Appl. Sci. Res. 2: 4. 86-93.

17.Liu, G.Q. 2011. Comparison of Regression and ARIMA models with Neural Network models to forecast the daily streamflow. PhD thesis, University of Delaware. 545p.

18.Misra, D., Oommen, T., Agarwal, A., and Mishra, S.K. 2009. Application and analysis of Support Vector machine based simulation for runoff and sediment yield. J. Biosyst. Engin. 103: 9. 527-535.

19.Moharrampour, M., Mehrabi, A., Hajikandi, H., and Sohrabi, S. 2013. Comparison of Support Vector Machines (SVM) and Autoregressive integrated moving average (ARIMA) in daily flow forecasting. J. River Engin. 1: 1. 34-45.

20.Salas, J.D. 1993. Analysis and modeling of hydrological time series. P 1-19, In: R, David (Ed.), Handbook of Hydrology, McGraw-Hill, New York.

21.Vapnik, V., and Chervonenkis, A. 1991. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis. 1: 3. 283-305.

22.Vapnik, V., and Cortes, C. 1995. Support vector networks. Machine Learning. 20: 273-297.

23.Wei, W.W.S. 2006. Time Series analysis: univariate and multivariate methods (second edition). Greg Tobin Publisher, ISBN 0-321-32216-9.

24.Yang, K., Shan, G., and Zhao, L. 2006. Correlation Coefficient Method for Support Vector Machine Input Samples. International Conference on Machine Learning and Cybernetics. Pp: 2857-2861.

25.Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., and Lee, K.K. 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J. Hydrol. 396: 1-2. 128-138.

26.Zhang, G.P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neuro computing. 50: 159-175.