Prediction of Groundwater Level using MODFLOW, Extreme Learning Machine and Wavelet-Extreme Learning Machine Models

In this study, the groundwater level of the Kabodarahang aquifer located in Iran, Hamadan Province is simulated using the MODFLOW, Extreme Learning Machine (ELM) and Wavelet-Extreme Learning Machine (WA-ELM) Models. Analysis of modeling results shows that numerical models simulate groundwater level with acceptable accuracy. For example, the correlation coefficient and scatter index values for the MODFLOW model are calculated 0.917 and 0.0004, respectively. Then, by different input combination and using the stepwise selection, 10 different models are introduced for the ELM and WA-ELM models as different lags. By evaluating all activation functions of the ELM model, the sigmoid activation function predicts groundwater level values with more accuracy. Also, Daubechies2 is chosen as the mother wavelet of the WA-ELM models. According to different numerical models results, the WA-ELM model is selected as the superior model in prediction of groundwater level. For the superior model, the correlation coefficient and Nash-Sutcliffe efficiency coefficient are calculated 0.959 and 0.915, respectively.