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A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

  • Maryam Malekzadeh
  • Saeid KardarEmail author
  • Keivan Saeb
  • Saeid Shabanlou
  • Lobat Taghavi
Article
  • 28 Downloads

Abstract

In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have occurred. This study models groundwater level (GWL) of the Kabodarahang region using two novel techniques including Self-Adaptive Extreme Learning Machine (SAELM) and Wavelet-Self-Adaptive Extreme Learning Machine (WA-SAELM). Using the stepwise selection as different lags along with different input combinations, ten different SAELM and WA-SAELM models were developed. First, the best activation function is chosen for numerical models. After that, GWL values were normalized to equalize the values and enhance speed and accuracy of modeling. Then, an optimized mother wavelet is selected in order to simulate GWLs. Next, the best model was introduced as the superior model in which values of the correlation coefficient (R), Root Mean Squared Error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were obtained 0.969, 0.358 and 0.939, respectively. In addition, the results of the superior model are compared with classical neural network models such as Artificial Neural Network (ANN), Wavelet-Artificial Neural Network (WA-ANN), Support Vector Machine (SVM) and Wavelet-Support Vector Machine (WA-SVM). Among all models, WA-SAELM approximated GWLs with higher accuracy. Furthermore, based on the results obtained from an uncertainty analysis, the superior model was identified as a model with an underestimated performance. Additionally, an explicit and practical matrix was proposed for computing GWLs. Finally, the matrix was validated for another piezometer.

Keywords

Groundwater Self-adaptive extreme learning machine Uncertainty analysis Wavelet transform Artificial neural network Support vector machine 

Notes

Compliance with Ethical Standards

Conflict of Interest

None.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Maryam Malekzadeh
    • 1
  • Saeid Kardar
    • 2
    Email author
  • Keivan Saeb
    • 3
  • Saeid Shabanlou
    • 4
  • Lobat Taghavi
    • 1
  1. 1.Department of Environmental Science, Faculty of Natural Resources and Environment, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Architecture, Faculty of Civil, Art and Architecture, Science and Research BranchIslamic Azad UniversityTehranIran
  3. 3.Department of Environment, Tonekabon BranchIslamic Azad UniversityTonekabonIran
  4. 4.Department of Water Engineering, Kermanshah BranchIslamic Azad UniversityKermanshahIran

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