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Comparison of machine learning models for predicting groundwater level, case study: Najafabad region

  • Research Article - Hydrology
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Abstract

Water resources, consisting of surface water and groundwater, are considered to be among the crucial natural resources in most arid and semiarid regions. Groundwater resources as the sustainable yields can be predicted, whereas this is one of the important stages in water resource management. To this end, several models such as mathematical, statistical, empirical, and conceptual can be employed. In this paper, machine learning and deep learning methods as conceptual ones are applied for the simulations. The selected models are support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron (MLP). Next, these models are optimized with the adaptive moment estimation (ADAM) optimization algorithm which results in hybrid models. The hyper-parameters of the stated models are optimized with the ADAM method. The root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are used to evaluate the accuracy of the simulated groundwater level. To this end, the aquifer hydrograph is used to compare the results with observations data. So, the RMSE and R2 show that the accuracy of the machine learning and deep learning models is better than the numerical model for the given data. Moreover, the MSE is approximately the same in all three cases (ranging from 0.7113 to 0.6504). Also, the total value of R2 and RMSE for the best hybrid model is 0.9617 and 0.7313, respectively, which are obtained from the model output. The results show that all three techniques are useful tools for modeling hydrological processes in agriculture and their computational capabilities and memory are similar.

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Funding

Payam Zarafshan reports financial support was provided by University of Tehran.

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Authors and Affiliations

  1. Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

    Pejman Zarafshan, Saman Javadi, Abbas Roozbahani & S. Mehdi Hashemy

  2. Department of Agro-Technology, College of Aburaihan, University of Tehran, Tehran, Iran

    Hamed Etezadi & Payam Zarafshan

  3. Center for Sustainable Development (CSD), College of Arts and Sciences, Qatar University, Doha, Qatar

    Abbas Roozbahani

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  1. Pejman Zarafshan
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  2. Hamed Etezadi
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  3. Saman Javadi
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  4. Abbas Roozbahani
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  5. S. Mehdi Hashemy
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  6. Payam Zarafshan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Pejman Zarafshan, Hamed Etezadi, Saman Javadi, Abbas Roozbahani, S. Mehdi Hashemy, and Payam Zarafshan. The first draft of the manuscript was written by Pejman Zarafshan, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Payam Zarafshan.

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Edited by Dr. Luigi Cimorelli (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Zarafshan, P., Etezadi, H., Javadi, S. et al. Comparison of machine learning models for predicting groundwater level, case study: Najafabad region. Acta Geophys. 71, 1817–1830 (2023). https://doi.org/10.1007/s11600-022-00948-8

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