In present study, the groundwater level of the Kabodarahang region located in Hamadan Province was simulated using novel techniques such as Self-Adaptive Extreme Learning Machine (SAELM) and WaveletSelf-Adaptive Extreme Learning Machine (WA-SAELM). Firstly, the effective lags were detected using the autocorrelation function and then ten models were developed for each SAELM and WA-SAELM methods. By evaluating the results of the models, WA-SAELM was introduced as the superior method. The analysis of the simulation results showed that the superior model had a high accuracy in estimating the groundwater level. For the superior model, the correlation coefficient (R), Root Mean Squared Error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were calculated to be 0. 969, 0. 358 and 0. 939, respectively.