1. Abbasian, M., Moghim, S. and Abrishamchi, A., 2019. Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135: 1465-1483.
2. Affandi, A.K. and Watanabe, K., 2007. Daily groundwater level fluctuation forecasting using soft computing technique. Nature and Science, 5(2): 1-10.
3. Bahmani, R. and Ouarda, T.B., 2021. Groundwater level modeling with hybrid artificial intelligence techniques. Journal of hydrology, 595, p.125659.
4. Chang, F.J. and Chang, Y.T., 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in water resources, 29(1): 1-10.
5. Chen, S.T. and Yu, P.S., 2007. Real-time probabilistic forecasting of flood stages. Journal of Hydrology, 340(1-2): 63-77.
6. Endo, H., Kitoh, A., Ose, T., Mizuta, R. and Kusunoki, S., 2012. Future changes and uncertainties in Asian precipitation simulated by multiphysics and multi–sea surface temperature ensemble experiments with high‐resolution Meteorological Research Institute atmospheric general circulation models (MRI‐AGCMs). Journal of Geophysical Research: Atmospheres, 117(D16).
7. Khosravi, K., Nohani, E., Maroufinia, E. and Pourghasemi, H.R., 2016. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83: 947-987.
8. Li, T., Zhang, L. and Murakami, H., 2015. Strengthening of the Walker circulation under globalwarming in an aqua-planet general circulation model simulation. Advances in Atmospheric Sciences, 32: 1473-1480.
9. Lin, J.Y., Cheng, C.T. and Chau, K.W., 2006. Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4): 599-612.
10. Moravej, M., Amani, P. and Hosseini-Moghari, S.M., 2020. Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR). Groundwater for Sustainable Development, 11, p.100447.
11. Nagy, H.M., Watanabe, K.A.N.D. and Hirano, M., 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering, 128(6): 588-595.
12. Nourani, V., Komasi, M. and Mano, A., 2009. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resources Management, 23: 2877-2894.
13. Nourani, V., Molajou, A., Tajbakhsh, A.D. and Najafi, H., 2019. A wavelet based data mining technique for suspended sediment load modeling. Water Resources Management, 33: 1769-1784.
14. Ostu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans SMC, 9 (1): 62-66.
15. Pijarski, P. and Kacejko, P., 2019. A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization, 51(12): 2049-2068.
16. Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E. and Rhodin, A., 2003. The atmospheric general circulation model ECHAM 5. PART I: Model description.
17. Shin, K.S., Lee, T.S. and Kim, H.J., 2005. An application of support vector machines in bankruptcy prediction model. Expert systems with applications, 28(1): 127-135.
18. Vapnik, V. and Chervonenkis, A., 1991. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis, 1(3): 283-305.
19. Vapnik, V.N., 1995. The nature of statistical learning theory. Springer, New York, 4: 250-320.
20. Vapnik, V.N., 1998. Statistical learning theory. Wiley, New York, 5: 250-320.
21. Wang, D., Romagnoli, J.A. and Safavi, A.A., 2000. Wavelet‐based adaptive robust M‐estimator for nonlinear system identification. AIChE Journal, 46(8): 1607-1615.
22. Kardan Moghaddam, H., Ghordoyee Milan, S., Kayhomayoon, Z., Rahimzadeh Kivi, Z. and Arya Azar, N., 2021. The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environ Monit Assess, 193(4): 1-20.