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Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components


Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management. Compared with alternative numerical models, soft computing methods are promising tools for GWL prediction, which need more hydrogeological and aquifer characteristics. The central aim of this research is to explore the performance of such well-accepted data-driven models to predict monthly GWL with emphasis on major meteorological components, including; precipitation (P), temperature (T), and evapotranspiration (ET). Artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least-square support vector machine (LSSVM) are used to predict one-, two-, and three-month ahead GWL in an unconfined aquifer. The main meteorological components (Tt, ETt, Pt, Pt-1) and GWL for one, two, and three lag-time (GWLt-1, GWLt-2, GWLt-3) are used as input to attain precise prediction. The results show that all models could have the best prediction for one month ahead in scenario 5, comprising inputs of GWLt-1, GWLt-2, GWLt-3, Tt, ETt, Pt, Tt-1, ETt-1, Pt-1. Based on different evaluation criteria, all employed models could predict the GWL with a desirable accuracy, and the results of LSSVM are the superior one.

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Availability of Data and Materials

The data, models, and codes generated or used during the study are available from the corresponding author by request.


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The authors acknowledge the Qazvin Regional Water Authority for providing part of the data.


This research received no specific grant from any funding agency.

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Authors and Affiliations



S.Smani and M. Vadiati analyzed and interpreted data and contributed to writing the manuscript. E.Zamani and Farahnaz Azizi collected data and contributed to drafting manuscript preparation. O.Kisi was involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

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Correspondence to Meysam Vadiati.

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Samani, S., Vadiati, M., Azizi, F. et al. Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components. Water Resour Manage (2022).

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  • Soft computing
  • Groundwater level prediction
  • Hydrogeology
  • Meteorological components