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Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data

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Abstract

The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The input combinations determined using auto-, partial auto- and cross-correlation analyses and tried for each model are: (i) GWL t−1 and GWL t−2; (ii) GWL t−1, GWL t−2 and P t ; (iii) GWL t−1, GWL t−2 and E t ; (iv) GWL t−1, GWL t−2, P t and E t ; (v) GWL t−1, GWL t−2 and P t−1 where GWL t , P t and E t indicate the GWL, precipitation and evaporation at time t, individually. The optimal ANN-GA, ANN-PSO and ANN-ICA models were obtained by trying various control parameters. The best accuracies of the ANN-GA, ANN-PSO and ANN-ICA models were obtained from input combination (i). The mean square error accuracies of the ANN-GA and ANN-ICA models were increased by 165 and 124% using ANN-PSO model. The results indicated that the ANN-PSO model performed better than the other models in modeling monthly groundwater levels.

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Authors and Affiliations

  1. Center for Interdisciplinary Research, International Balck Sea University, Tbilisi, Georgia

    Ozgur Kisi

  2. Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Meysam Alizamir

  3. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Zounemat-Kermani

Authors
  1. Ozgur Kisi
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  2. Meysam Alizamir
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  3. Mohammad Zounemat-Kermani
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Correspondence to Ozgur Kisi.

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Kisi, O., Alizamir, M. & Zounemat-Kermani, M. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Nat Hazards 87, 367–381 (2017). https://doi.org/10.1007/s11069-017-2767-9

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  • DOI: https://doi.org/10.1007/s11069-017-2767-9

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