Adnan M, Liang Z, Heddam S, Zounemat Kermani M, Kisi O, Li B (2019) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of hydrology, 580: 983-993
 Aggarwal SK, Goel A, Singh VP (2012) Stage and discharge forecasting by SVM and ANN techniques. Water ResourManag 26:3705–3724
 Asefa T, Kemblowski M, McKee M, Khalil A (2005) Multi-time scale stream flow predictions: The support vector machines approach. J. of Hydrology, 318 (1-4): 7-16.
 Barzegar R, Ghasri M, Qi Z, Quilty J, Adamowski J (2019) Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada. Journal of Hydrology, 577:880-903
 Bhagwat PP, Maity R (2012) Multistep-ahead river flow prediction using LS-SVR at daily scale. J Water Resource Prot 4:528–539
 Buyukyildiz M, Kumcu SY (2017) An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models. Water Resources Management 31(2):1-17
 Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civil Eng 15:208–216
 Duan QY, Sorooshian S, Gupta VK (1994) Optimal use of the SCEUA global optimization method for calibrating watershed models. J Hydrol 158:265–284
 Elkiran G, Nourani V, Abba SL (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577:962-974
 Fathian F, Mehdizadeh S, KozekalaniSales A, Mir J, Safari S (2019) Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models. Journal of Hydrology, 575:1200-1213
 Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, Vol.13(2): 87–129.
 Ghazvinei T, ShamshirbandSh, MotamediSh, Hassanpour H, Salwana E (2017) Performance investigation of the dam intake physical hydraulic model using Support Vector Machine with a discrete wavelet transform algorithm. Computers and Electronics in Agriculture, 140:48-57
 Ghorbani MA, Khatibi R, Asadi H, Yousefi P (2012) Inter- Comparison of an Evolutionary Programming Model of Suspended Sediment Time-series whit other Local Model. INTECH.doi. org/10.5772/47801,. 255-282
 Ghorbani MA, KhatibiR,Goel A, FazeliFard MH, Azani A (2016) Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci. DOI: 10.1007/s12665-016-5435-6
 Goel A, Pal M (2012) Stage–discharge modeling using support vector machines.IJE Trans A Basics. doi:10.5829/idosi.ije.2012.25. 01a.01
 Guven A (2009) Linear genetic programming for time-series modelling of daily flow rate. Journal Earth System Science, vol 118, 157-173.
 He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509:379-386
 Imani M, Chao H, Lan W, Kuo C (2018) Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Global and Planetary Change, 161:211-221
 Jayawardena AW, Muttil N, Fernando TMKG (2005) Rainfall-Runoff ModellingusingGenetic Programming. International Congress on Modelling and Simulation Society of Australia and New Zealand December: 1841-1847..
 Karahan H, Iplikci S, Yasar M, Gurarslan G (2014) River Flow Estimation from Upstream Flow Records Using Support Vector Machines. Journal of Applied Mathematics.14(7):7-14
 Khatibi R, Naghipour L, Ghorbani MA, Aalami MT (2012) Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations. Neural computing and application: 643-941.
 Kisi O, Shiri J (2012) River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques. Computers & Geosciences,43(4):73-82
 Kisi O, Cobaner M (2009) Modeling river stage–discharge relationships using different neural network computing techniques. Clean Soil Air Water 37:160–169
 Lin J, Cheng C, Chau K (2006) Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal.51(4):599-610.
 Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines. J Am Water Resour 38:173–186
 Londhe S, Gavraskar S (2018) Stream Flow Forecasting using Least Square Support Vector Regression. Journal of Soft Computing in Civil Engineering , 2(4):56-88
 Misra D, Oommen T, Agarwal A, Mishra SK, Thompson AM (2009) Application and analysis of support vector machine based simulation for runoff and sediment yield. BiosystEng 103:527–535
 Moharrampour M, Mehrabi A, Katouzi M (2012) Daily Discharge Forecasting Using Support Vector Machine. International Journal of Information and Electronics Engineering.2(5):769-772
 Nourani V, Alizadeh A, Roushangar K (2016) Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load. Water Resources Management,30(1):393-407
 Rezaei M, Kim B, Fallah H, Alghmand S (2019) Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea. Journal of Hydrology,572:470-485
 Sahoo BB, Jha R, Singh A, Kumar D (2019) Application of Support Vector Regression for Modeling Low Flow Time Series. Water Resources and Hydrologic Engineering, 23:923-934
 Shin S, Kyung D, Lee S, Taik& Kim J, Hyun J (2005) An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28: 127-135.
 Sivapragasam C, Muttil N (2005) Discharge rating curve extension a new approach. Water ResourManag 19:505–520
 Tongal H, Booij M (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of Hydrology, 564:266-282
 Vapnik VN (1998) Statistical Learning Theory. Wiley, New York.
 Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558
 Xu L, Wang J, Guan J, Huang F (2007) A Support Vector Machine Model for Mapping of Lake Water Quality from Remote-Sensed Images. IC-MED. Vol. 1(1), pp. 57-66.
 Yassen ZM, Awadh SM, Sharafati A, Shahid S (2018) Complementary data-intelligence model for river flow simulation. Journal of Hydrology, 567:190-190
 Yu PS, Chen ST, Chang IF (2006) Support vector regression for realtime flood stage forecasting. J Hydrol 328:704–716