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Application of Several Data-Driven Techniques for Predicting Groundwater Level

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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.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran

    Bagher Shirmohammadi, Mehdi Vafakhah & Vahid Moosavi

  2. Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Alireza Moghaddamnia

Authors
  1. Bagher Shirmohammadi
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  2. Mehdi Vafakhah
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  3. Vahid Moosavi
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  4. Alireza Moghaddamnia
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Correspondence to Mehdi Vafakhah.

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Shirmohammadi, B., Vafakhah, M., Moosavi, V. et al. Application of Several Data-Driven Techniques for Predicting Groundwater Level. Water Resour Manage 27, 419–432 (2013). https://doi.org/10.1007/s11269-012-0194-y

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  • DOI: https://doi.org/10.1007/s11269-012-0194-y

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