Prediction of Groundwater Level Changes Using Hybrid Wavelet Self- Adaptive Extreme Learning Machine Model- Observation Well of Sarab Qanbar, Kermanshah
Yosevfand, F. | Shabanlou, S.
In this study, the groundwater level (GWL) of the Sarab Qanbar region located in the south of Kermanshah, Iran, was estimated using the Wavelet- Self- Adaptive Extreme Learning Machine (WA- SAELM) model. An artificial intelligence method called “Self- Adaptive Extreme Learning Machine” and the “Wavelet transform” method were implemented for developing the numerical model. First, by using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and the effective lags in estimating GWL, eight distinctive SAELM and WA- SAELM models were developed. Later, the values of the observational well were normalized for estimating GWL. Next, the most optimized mother wavelet was chosen for the modeling. By evaluating the results of SAELM and WA- SAELM, it was concluded that the WA- SAELM models could estimate the values of the objective function with higher accuracy. Then, the superior model was introduced, showing that it could be very accurate in forecasting the GWL. In the test mode, for example, the values of R (correlation coefficient), Main absolute error (MAE) and the NSC- Sutcliffe efficiency coefficient (NSC) for the superior model were calculated to be 0.995, 0.988 and 0.990, respectively. Furthermore, an uncertainty analysis was conducted for the numerical models, proving that the superior model had an underestimated performance.
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