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Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer)

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Abstract

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2 months, but the models provided unsatisfactory results for a 3-month leading time.

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Data availability statement

Data will be made available on reasonable request.

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Authors and Affiliations

  1. Global Affairs, Hubert H. Humphrey Fellowship Program, University of California, 10 College Park, Davis, CA, 95616, USA

    Meysam Vadiati

  2. Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran

    Zahra Rajabi Yami & Mohammad Nakhaei

  3. Department of Geology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran

    Effat Eskandari

  4. Department of Architecture and Civil Engineering, University of Applied Sciences Lübeck, 23562, Lübeck, Germany

    Ozgur Kisi

  5. Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia

    Ozgur Kisi

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  1. Meysam Vadiati
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Vadiati, M., Rajabi Yami, Z., Eskandari, E. et al. Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer). Environ Monit Assess 194, 619 (2022). https://doi.org/10.1007/s10661-022-10277-4

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