Babaei, M., Moeini, R. & Ehsanzadeh, E. (2019). Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir). Water Resources Management, 33(6), 2203-2218.
Bafitlhile, T.M. & Li, Z. (2019). Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China. Water, 11(1), 85-96.
Chen, Q. Dai, G. & Liu, H. (2002). Volume of fluid model for turbulence numerical simulation of stepped spillway overflow. Journal of Hydraulic Engineering, 128(7), 683-688.
Chen, S.T. & Yu, P.S. (2007). Real-time probabilistic forecasting of flood stages. Journal of Hydrology, 340(1-2), 63-77.
Dehghani, M., Seifi, A., & Riahi-Madvar, H. (2019). Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and Grey Wolf optimization. Journal of Hydrology.
Falehi, A. D. (2018). MOPSO based TCSC–ANFIS–POD technique: Design, simultaneous scheme, power system oscillations suppression. Journal of Intelligent & Fuzzy Systems, 34(1), 23-34.
Foroudi Khowr, A., Saneie, M. & Azhdari Moghaddam, M. (2017). Comparison of Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM) for discharge capacity prediction of a sharp-crested weirs. Iranian Journal of Irrigation & Drainage, 11(5), 772-784. (In Farsi)
Isazadeh, M., ahmadzadeh, H. & Ghorbani, M. (2016). Assessment of Kernel Functions Performance in River Flow Estimation using Support Vector Machine. Journal of Water and Soil Conservation, 23(3), 69-89.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. (In Farsi)
Khazaee Poul, A. K., Shourian, M., & Ebrahimi, H. (2019). A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction. Water Resources Management, 1-17.
Kia, I., Emadi, A., Gholami, M. (2019). Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR). Irrigation and Water Engineering, 9(4), 39-51. (In Farsi)
Lohani, A. K., Kumar, R., & Singh, R. D. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442, 23-35.
Mantero, P., Moser, G., & Serpico, S. B. (2005). Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 559-570.
Mozaiyan, M., Akhoond Ali, A., Massah Bavani3, A., Radmanesh, F., Zohrabi, N. (2015). The Impact of Climate Change on Low Flows (Case Study: Sepid Dasht Sezar). Irrigation Sciences and Engineering, 38(2), 1-19. (In Farsi)
Pham, Q. B., Yang, T. C., Kuo, C. M., Tseng, H. W., & Yu, P. S. (2019). Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling. Water, 11(3), 45-59.
Rehana, S. (2019). River Water Temperature Modelling Under Climate Change Using Support Vector Regression. In Hydrology in a Changing World (pp. 171-183). Springer, Cham.
Rezaei, E., Khashei- Siuki, A., 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), 389-396. (In Farsi)
Riahi-Madvar, H., Dehghani, M., Seifi, A., Salwana, E., Shamshirband, S., Mosavi, A., & Chau, K. W. (2019). Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Engineering Applications of Computational Fluid Mechanics, 13(1), 529-550.
Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert systems with applications, 28(1), 127-135.
Vapnik, V.N. (1998). Statistical Learning Theory. Wiley, New York.
Wu, J., Liu, H., Wei, G., Song, T., Zhang, C., & Zhou, H. (2019). Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment. Water, 11(7), 13-27.
Zaini, N., Malek, M. A., Yusoff, M., Mardi, N. H., & Norhisham, S. (2019). Daily River Flow Forecasting with Hybrid Support Vector Machine–Particle Swarm Optimization. In IOP Conference Series: Earth and Environmental Science (Vol. 140, No. 1, p. 012035). IOP Publishing.
Zhou, Y., Guo, S., & Chang, F. J. (2019). Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. Journal of hydrology, 570, 343-355.
)