Belayneh, A., Adamowski, J., Khalil, B. and Ozga-Zielinski, B. (2014). "Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models," Journal of Hydrology, vol. 508, pp. 418-429.
Adnan, R. M., Yuan, X., Kisi, O., & Yuan, Y. (2017). Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 29(1), 286-294.
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.
Chiang, J. L., & Tsai, Y. S. (2012). Reservoir drought prediction using support vector machines. In Applied Mechanics and Materials (Vol. 145, pp. 455-459). Trans Tech Publications.
Choy, K. Y., & Chan, C. W. (2003). Modelling of river discharges and rainfall using radial basis function networks based on support vector regression. International Journal of Systems Science, 34(14-15), 763-773.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Foroughi, F., Araghinejad, S. (2017). Long-lead streamflow forecasting using singular spectrum analysis in the Karkheh basin. Iranian Journal of Soil and Water Research, 48(2), 309-321. doi: 10.22059/ijswr.2017.62633. (in Farsi)
Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water Resources Management, 30(1), 375-391.
Guo, J., Zhou, J., Qin, H., Zou, Q., & Li, Q. (2011). Monthly streamflow forecasting based on improved support vector machine model. Expert Systems with Applications, 38(10), 13073-13081.
Jamali, B., Ebrahimi, K. (2010). water quality time series forecasting using linear models random case study: Sefid Rud river. Agricultural Engineering Research, 12 (3), 31-44. doi: 10.22092/jaer.2011.100317. (in Farsi)
Kisi, O. (2015). Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water resources management, 29(14), 5109-5127.
Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4), 599-612.
Modaresi F., Araghinejad S., Ebrahimi K. (2017a). Assessment of Ordered Weighted Averaging Strategies in Combination of Streamflow Forecasting Models. jwmseir. 10 (35):15-25. URL: http://jwmsei.ir/article-1-469-fa.html
Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2017b). A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions. Water Resources Management, 1-16.
Modaresi, F., & Araghinejad, S. (2014). A comparative assessment of support vector machines, probabilistic neural networks, and K-nearest neighbor algorithms for water quality classification. Water resources management, 28(12), 4095-4111.
Nieto, P. G., García-Gonzalo, E., Fernández, J. A., & Muñiz, C. D. (2014). Hybrid PSO–SVM-based method for long-term forecasting of turbidity in the Nalón river basin: A case study in Northern Spain. Ecological Engineering, 73, 192-200
Peña-Guzmán, C., Melgarejo, J., & Prats, D. (2016). Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines. Mathematical Problems in Engineering, 2016.
Seyam, M., Othman, F., & El-Shafie, A. (2017). Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines. In MATEC Web of Conferences (Vol. 111, p. 01007). EDP Sciences.
Sivapragasam, C., Liong, S. Y., & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA–SVM approach. Journal of Hydroinformatics, 3(3), 141-152.
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.
Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281-287).
Wang, W., Nie, X., & Qiu, L. (2010, October). Support vector machine with particle swarm optimization for reservoir annual inflow forecasting. In Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on (Vol. 1, pp. 184-188). IEEE.
Wen, X., Si, J., He, Z., Wu, J., Shao, H., & Yu, H. (2015). Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water Resources Management, 29(9), 3195-3209.
Yu, X., Liong, S. Y., & Babovic, V. (2004). EC-SVM approach for real-time hydrologic forecasting. Journal of Hydroinformatics, 6(3), 209-223.
Yu, X., Zhang, X., & Qin, H. (2018). A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting. Journal of Hydro-environment Research, 18, 12-24.