%0 Journal Article
%T Simulation of Groundwater Level Using the Hybrid Model Wavelet-Self Adaptive Extreme Learning Machine
%J Iranian Journal of Soil and Water Research
%I University of Tehran
%Z 2008-479X
%A malekzadeh, maryam
%A kardar, saeid
%A shabanlou, saeid
%D 2020
%\ 06/21/2020
%V 51
%N 4
%P 975-986
%! Simulation of Groundwater Level Using the Hybrid Model Wavelet-Self Adaptive Extreme Learning Machine
%K Ground water
%K Self-adaptive extreme learning machine
%K Wavelet transform
%K Wavelet-Self-Adaptive Extreme Learning Machine
%K Kabodarahang
%R 10.22059/ijswr.2020.292367.668394
%X 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.
%U https://ijswr.ut.ac.ir/article_74775_5755a7495e91c98f6d5c5a8c8a5d9598.pdf