Cao, J., Lin, Z. and Huang, G. B. (2012) Self-adaptive evolutionary extreme learning machine. Neural Process. Lett., 36, 285–305.
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.
Chitsazan, M., Rahmani, G. and Neyamadpour, A. (2013) Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran. Geopersia, 3(1), 35-46.
Coppola, E. A., Rana, A. J., Poulton, M. M., Szidarovszky, F. and Uhl, V. W. (2005) A neural network model for predicting aquifer water level elevations. Groundwater, 43(2), 231-241.
Daliakopoulos, I. N., Coulibaly, P. and Tsanis, I. K. (2005) Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1), 229-240.
Dash, N. B., Panda, S. N., Remesan, R. and Sahoo, N. (2010) Hybrid neural modeling for groundwater level prediction. Neural Computing and Applications, 19(8), 1251-1263.
Ebrahimi, H. and Rajaee, T. (2017) Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181-191.
Emamgholizadeh, S., Moslemi, K. and Karami, G. (2014) Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water resources management, 28(15), 5433-5446.
Huang, G. B., Zhu, Q. Y. and Siew, C. K. (2006) Extreme learning machine: theory and applications. Neurocomputing, 70(1), 489-501.
Huang, G. B., Zhu, Q. Y. and Siew, C. K. (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. In Proc. IJCNN, Budapest, Hungary, Jul. 25–29, 2, 985–990.
Khaki, M., Yusoff, I. and Islami, N. (2015) Simulation of groundwater level through artificial intelligence system. Environmental Earth Sciences, 73(12), 8357-8367.
Kisi, O. and Shiri, J. (2012) Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations. Hydrology Research, 43(3), 286-300.
Malekzadeh, M., Kardar, S. and Shabanlou, S. (2019). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models. Groundwater for Sustainable Development, 9, 100279.
Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J. M. (1996) Wavelet Toolbox for Use with Matlab. The Mathworks, Inc.: Natick, Massachusetts, USA.
Nayak, P. C., Rao, Y. S. and Sudheer, K. P. (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management, 20(1), 77-90.
Nourani, V., Mogaddam, A. A. and Nadiri, A. O. (2008) An ANN‐based model for spatiotemporal groundwater level forecasting. Hydrological Processes: An International Journal, 22(26), 5054-5066.
Silhavy, R., Silhavy, P. and Prokopova, Z. (2017) Analysis and selection of a regression model for the use case points method using a stepwise approach. J. Syst. Softw. 125, 1-14
Storn, R. and Price, K. (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.