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

Saeideh Samani (), Meysam Vadiati (), Farahnaz Azizi (), Efat Zamani () and Ozgur Kisi ()
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Saeideh Samani: Water Research Institute (WRI)
Meysam Vadiati: Hubert H. Humphrey Fellowship Program, Global Affairs, University of California
Farahnaz Azizi: Kohgiluyeh and Boyerahmad Regional Water Company
Efat Zamani: Iranian Water Resources Management Company (WRM)
Ozgur Kisi: University of Applied Sciences

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 10, No 13, 3627-3647

Abstract: Abstract 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.

Keywords: Soft computing; Groundwater level prediction; Hydrogeology; Meteorological components (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03217-x

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