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Application of Several Data-Driven Techniques for Predicting Groundwater Level

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Listed:
  • Bagher Shirmohammadi

    ()

  • Mehdi Vafakhah

    ()

  • Vahid Moosavi

    ()

  • Alireza Moghaddamnia

Abstract

In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and system identification models. ANFIS model in which preprocessed data using fuzzy interface system is used as input for artificial neural network (ANN) can cope with non-linear nature of time series so it can perform better than others. It was also demonstrated that all above mentioned approaches could model groundwater level for 1 and 2 months ahead appropriately but for 3 months ahead the performance of the models was not satisfactory. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
  • Handle: RePEc:spr:waterr:v:27:y:2013:i:2:p:419-432
    DOI: 10.1007/s11269-012-0194-y
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    References listed on IDEAS

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    Cited by:

    1. Jiyang Tian & Chuanzhe Li & Jia Liu & Fuliang Yu & Shuanghu Cheng & Nana Zhao & Wan Zurina Wan Jaafar, 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-17, October.
    2. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
    3. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    4. González Perea, R. & Camacho Poyato, E. & Montesinos, P. & Rodríguez Díaz, J.A., 2018. "Prediction of applied irrigation depths at farm level using artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 206(C), pages 229-240.

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