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A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer

  • Research Article - Hydrology
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Abstract

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, the depth of wells has increased to access water. In some areas, the groundwater depth is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the groundwater depth to provide a regional model with multiple linear regression (MLR) methods for Arak plain aquifer. For this purpose, the average groundwater potential maps (GPMs) in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GPMs for the region were created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root-mean-square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GPMs drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values at the maximum operating radius of the well, and the distance from water resources are the main factors of GPMs drawdown. But the effectiveness of ANN in estimating GPMs drawdown is higher than the MLR method. The implemented methodology could be generalized to other watersheds with water scarcity problems for groundwater management.

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Acknowledgements

We would like to thank the MRWA for providing the secondary data on groundwater.

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Authors and Affiliations

  1. Department of Water Engineering, Arak Branch, Islamic Azad University, Arak, Iran

    Soadat Hanifian & Reza Jafarnia

  2. Department of Range and Watershed Management, Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran

    Mohammad Reza Khaleghi

  3. Department of Water Engineering, Faculty of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran

    Mohsen Najarchi

  4. Department of Natural Resources and Environmental Sciences, Arak Branch, Islamic Azad University, Arak, Iran

    Javad Varvani

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  1. Soadat Hanifian
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  2. Mohammad Reza Khaleghi
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  3. Mohsen Najarchi
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  4. Reza Jafarnia
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  5. Javad Varvani
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Correspondence to Mohammad Reza Khaleghi or Mohsen Najarchi.

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Edited by Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Hanifian, S., Khaleghi, M.R., Najarchi, M. et al. A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer. Acta Geophys. 72, 419–432 (2024). https://doi.org/10.1007/s11600-023-01050-3

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