Adamowski, J. and Prasher, S.O. (2012). “Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data”, Journal of Water and Land Development, 17(1), 89-97.
Aqil, M., Kita, I., Yano, A. and Nishiyama, S. (2007). “A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff”, Journal of Hydrology, 337(1-2), 22-34.
Barzegari, F., Yosefi, M. and Talebi, A. (2015). “Estimating suspended sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) models (Case study: Lorestan Province, Iran)”, Civil Engineering Infrastructures Journal, 48(2), 373-380.
, Bandyopadhyay, A.
, Singh, R. and
(2010). “Rainfall-runoff modeling: Comparison of two approaches with different data requirements, water resources management”, Water Resources Management
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone C.J. (1984). Classification and regression trees, Belmont, Wadsworth Statistical Press.
Dastorani, M.T., Moghadamnia, A.R., Piri, J. and Rico-Ramirez, M. (2010). “Application of ANN and ANFIS models for reconstructing missing flow data”, Environmental Monitoring and Assessment, 166(1-4), 421-434.
Etemad-Shahidi, A. and Mahjoobi, J. (2009). “Comparison between M5' model tree and neural networks for prediction of significant wave height in Lake Superior”, Ocean Engineering, 36(15-16), 1175-1181.
El-shafie, A., Mukhlisin, M., Najah, A.A. and Taha, M.R. (2011). “Performance of artificial neural network and regression techniques for rainfall-runoff prediction”, International Journal of the Physical Sciences, 6(8), 1997-2003.
Granata, F., Gargano, R. and Marinis, G. (2016). “Support vector regression for Rainfall-Runoff modeling in urban drainage: A comparison with the EPA’s storm water management model”, Water, 8(3), 1-13.
Huang, W. and Foo, S. (2002). “Neural network modelling of salinity variation in Apalachicola river”, Water Research, 36(1), 356-362.
Kamali, B. and Mousavi, S.J. (2014). “Automatic calibration of HEC-HMS model using Multi-Objective fuzzy optimal models”, Civil Engineering Infrastructures Journal, 47(1), 1-12.
Karimaee Tabarestani, M. and Zarrati, A.R. (2015). “Design of riprap stone around bridge piers using empirical and neural network method”, Civil Engineering Infrastructures Journal, 48(1), 175-188.
Mahjoobi, J. and Adeli Mosabbeb, E. (2009). “Prediction of significant wave height using regressive support vector machines”, Ocean Engineering
, 36(5), 339-347.
Platt, J. (1999). “Fast training of support vector machines using sequential minimal optimization”, Advances in Kernel Methods, Support Vector Learning, Schölkopf_Burges, C.J.C. and Smola, A.J., (eds.), Cambridge, MA, MIT Press, 185-208.
Quinlan, J.R. (1992). “Learning with continuous classes”, Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, 343-348.
Shortridge, J.E., Guikema, S.D. and Zaitchik, B.F. (2016). “Machine learning methods for empirical streamflow simulation: A comparison of model accuracy, interpretability and uncertainty in seasonal watersheds”, Hydrological Earth System Sciences, 20, 2611-2628.
Smola, A.J. and Schölkopf, B. (1988). “A tutorial on support vector regression”, Royal Holloway College, London, U.K., NeuroCOLT Technology Report, TR 1998-030.
Vapnik, V. (1995). The nature of statistical learning tTheory, Springer, N.Y.
Wang, Y. and Witten, I.H. (1997). “Induction of model trees for predicting continuous lasses”, Proceedings of the Poster Papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
Yilmaz, A. and Muttil, N. (2014). “Runoff estimation by machine learning methods and application to the Euphrates Basin in Turkey”, Journal of Hydrologic Engineering, 19(5), 1015-1025.