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Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer

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      Abstract

      In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.

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      Journal
      Proceedings
      Proceedings
      MDPI AG
      2504-3900
      January 2018
      August 03 2018
      : 2
      : 11
      : 697
      10.3390/proceedings2110697
      © 2018

      https://creativecommons.org/licenses/by/4.0/

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      Self URI (article page): http://www.mdpi.com/2504-3900/2/11/697

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