Iran-Water Resources Research

Iran-Water Resources Research

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

Document Type : Technical Note (5 pages)

Authors
Maryam Malekzadeh 1
Saeid Kardar 2
Keivan Saeb 3
Saeid Shabanlou 4
Lobat Taghavi 1
1 Department of Environment and Energy, Science and Research branch, Islamic Azad University, Tehran, Iran
2 Department of Environment and Energy, Science and Research branch, Islamic Azad University, Tehran, Iran.
3 Department of Environment, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
4 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
Abstract
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.
Keywords
groundwater level
MODFLOW
Extreme learning machine
Wavelet
Wavelet-Extreme Learning Machine

Subjects

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Iran-Water Resources Research
Volume 14, Issue 5
Autumn 2018
Pages 496-501

  • Receive Date 07 February 2018
  • Revise Date 23 July 2018
  • Accept Date 27 July 2018