Simulation of Groundwater Level Using the Hybrid Model Wavelet-Self Adaptive Extreme Learning Machine

Document Type : Research Paper


1 Assistance Prof., Department of Environment, Faculty of enviroment, Tehran North Branch, Islamic Azad University, Tehran, Iran

2 Assistance Prof., Department of Architecture, Faculty of Art and Architecture, Science and Research Branch, Islamic Azad Univ., Tehran, Iran.

3 Associ. Prof., Dept. of Water Eng., Faculty of Agriculture, Kermanshah Branch, Islamic Azad Univ., Kermanshah, Iran.


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 Wavelet-Self-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.


Main Subjects

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