Asefa T, Kemblowski M, McKee M and Khalil A, 2006. Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology 318(1):7-16.
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):7624-7629.
Bouallègue S, Haggège J and Benrejeb M, 2012. A new method for tuning PID-type fuzzy controllers using particle swarm optimization. Pp.140-159, In: Fuzzy Controllers-Recent Advances in Theory and Applications.
Choy KY and Chan CW, 2003. Modeling 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.
Dibike YB, Velickov S, Solomatine D and Abbott MB, 2001. Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering 15 (3):208-216.
Feng ZK, Niu WJ, Tang ZY, Jiang ZQ, Xu Y, Liu Y and Zhang HR, 2020. Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology 583: 124627.
Han D, Chan L and Zhu N, 2007. Flood forecasting using support vector machines. Journal of Hydroinformatics 9(4):267-276.
Hsu KL, Gupta HV and Sorooshian S, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research 31(10):2517-2530.
Kennedy J and Eberhart RC, 1995. Particle swarm optimization. Pp.1942-1948, Proceedings of IEEE International Conference on Neural Networks IV, Piscataway, NJ: IEEE Press.
Komasi M and Sharghi S, 2014. Flood forecasting with artificial neural network wavelet multi-scale hybrid model. The Second National Conference on Management and Engineering Flood. 30 September-1 October, Tehran, Iran. (In Persian with English abstract).
Komasi M and Sharghi S, 2016. Hybrid wavelet-support vector machine approach for modeling rainfall–runoff process. Water Science and Technology 73(8):1937-1953.
Komasi M, Sharghi S and Safavi HR, 2018. Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using standardized precipitation index (Case study: Urmia Lake, Iran). Journal of Hydroinformatics 20(4): 975-988.
Lin GF, Chen GR, Huang PY and Chou YC, 2009. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology 372(1):17-29.
Mallat SG, 1998. A Wavelet Tour of Signal Processing. Second ed. Academic Press. San Diego.
Minns AW and Hall MJ, 1996. Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal 41(3):399-417.
Savic DA, Walters GA and Davidson JW, 1999. A genetic programming approach to rainfall-runoff modelling. Water Resources Management 13(3):219-231.
Shamseldin AY, 1997. Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology 199(3-4):272-294.
Smith J and Eli RN, 1995. Neural-network models of rainfall-runoff process. Journal of Water Resources Planning and Management 121(6):499-508.
Tikhamarine Y, Souag-Gamane D. Ahmed AN, Sammen SS, Kisi O, Huang YF and El-Shafie A, 2020. Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization. Journal of Hydrology 589: 125133.
Van den Bergh F and Engelbrecht AP, 2006. A study of particle swarm optimization particle trajectories. Information Sciences 176(8):937-971.
Vapnik V and Cortes C, 1995. Support vector networks. Machine Learning 20(3):273-297.
Vapnik VN, 1995. The Nature of Statistical Learning Theory. Springer, New York.
Wang W, Men C and Lu W, 2008. Online prediction model based on support vector machine. Neurocomputing 71(4):550-558.