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Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

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  • A. Izady
  • K. Davary
  • A. Alizadeh
  • A. Moghaddam Nia
  • A. Ziaei
  • S. Hasheminia

Abstract

There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network – Auto Regressive e Xtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the “Ward” method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R 2 = 0.97, RMSE = 0.03 m, ME = --0.07 m and R 2 = 0.81, RMSE = 0.35 m, ME = 0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • A. Izady & K. Davary & A. Alizadeh & A. Moghaddam Nia & A. Ziaei & S. Hasheminia, 2013. "Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4773-4794, November.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:14:p:4773-4794
    DOI: 10.1007/s11269-013-0432-y
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    References listed on IDEAS

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    1. D. Allen & N. Schuurman & Q. Zhang, 2007. "Using fuzzy logic for modeling aquifer architecture," Journal of Geographical Systems, Springer, vol. 9(3), pages 289-310, September.
    2. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
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    Cited by:

    1. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.

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