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Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems

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Abstract

Modeling the behavior of groundwater levels is necessary to implement sustainable groundwater resource management. Groundwater is a non-linear and complex system, which can be modeled by data-driven models. This study evaluates the performances of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead. This is the first time that confined and unconfined aquifers have been compared using data-driven models. In addition, to identify the optimal input combination, a hybrid gamma test (GT) and genetic algorithm (GA) was used. The coefficient of correlation (R), Mean Absolute Error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and developed discrepancy ratio (DDR) were applied to evaluate the SVR and ANN models. Results showed that the SVR and ANN models were more accurate for the unconfined system than the confined system for forecasts up to 3-month ahead. In both hydrogeological systems for 1-month ahead, the models performed better than for 2- and 3-month ahead forecasts, and the accuracy of the models decreased as the months ahead increased. The SVR model performed better than the ANN model for 1-, 2-, and 3-month ahead groundwater-level forecasting. The SVR model could be successfully used in predicting monthly groundwater in confined and unconfined systems.

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Abbreviations

AI:

Artificial intelligence

AMSL:

Above mean sea level

ANFIS:

Neuro-fuzzy inference system

ANN:

Artificial neural network

D:

River discharge

DDR:

Developed discrepancy ratio

E:

Evapotranspiration

ET:

Entropy theory

GA:

Genetic algorithm

GEP:

Gene expression programming

GP:

Genetic programming

GT:

Gamma Test

H:

Head

LM:

Levenberg–Marquardt

MAE:

Mean absolute error

MLP:

Multi-layer perceptron

NSE:

Nash–sutcliffe efficiency

P:

Precipitation

PCA:

Principal components analysis

Q:

Water well discharge

R:

Correlation coefficient

RBF:

Radial basis function

RMS:

Root mean squared error

SVM:

Support vector machine

SVR:

Support vector machine regression

T:

Temperature

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Authors and Affiliations

  1. Department of Mineral Geology and Hydrogeology, Shahid Beheshti University, Tehran, Iran

    A. Mirarabi, H. R. Nassery & F. Alijani

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

    M. Nakhaei

  3. Department of Bioresource Engineering, McGill University, Montreal, Canada

    J. Adamowski & A. H. Akbarzadeh

Authors
  1. A. Mirarabi
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  2. H. R. Nassery
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  3. M. Nakhaei
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  4. J. Adamowski
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  5. A. H. Akbarzadeh
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  6. F. Alijani
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Correspondence to H. R. Nassery.

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Mirarabi, A., Nassery, H.R., Nakhaei, M. et al. Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environ Earth Sci 78, 489 (2019). https://doi.org/10.1007/s12665-019-8474-y

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