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Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study—Shabestar Plain, Iran

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Abstract

Groundwater aquifers have always been confronted with significant challenges around the world such as climate change, over-extraction, pollution by wastewaters, and saltwater intrusion in coastal areas. Prediction of groundwater level under the effects of climate change is more important in water resource management. This study has therefore been evaluated the effects of two climate parameters (i.e., precipitation and temperature) in groundwater level for the Shabestar Plain, Iran. For this end, four models from General Circulation Models (GCM) were then used to evaluate future climate change scenarios of the Representative Concentration Pathway (i.e., RCP2.6, RCP4.5, RCP8.5). In the next phase, to reduce the spatial complexity of observation wells, clustering analysis was used. In case of groundwater level modeling, time series in the base period, Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Network with Exogenous inputs (NARX) were also used. To improve the prediction accuracy, time series preprocessing made by wavelet-based de-noising approach was used. Analysis of the results illustrates an increase in temperature and a decrease in precipitation for study region in the future period times. The results also reveal that hybrid techniques of the wavelet-NARX give best results in comparison with the other models. A simulation result illustrates that the groundwater level declines in RCP2.6, 4.5, and 8.5, which gives average levels of 0.61, 0.81, and 1.53 m, respectively, for the future period years (i.e., 2020–2024). These results would lead to continuous groundwater depletion. These findings emphasize the necessity of the importance of extraction policies in water resource management.

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Abbreviations

AI:

Artificial intelligence

ANFIS:

Adaptive Neuro-Fuzzy Inference System

ANN:

Artificial Neural Network

ARX:

Autoregressive Exogenous Model

BNU:

Beijing Normal University, China

CCSM4:

Community Climate System Model, USA

CMIP5:

Fifth Assessment Report of IPCC

FN:

Feedforward network

EC-EARTH:

European Community Earth-System Model, Greece

GCM:

General Circulation Model

GFDL:

Geophysical Fluid Dynamics Laboratory, USA

IPCC:

Intergovernmental Panel on Climate Change

LSSVM:

Least Square Support Vector Machine

LARS-WG:

Long Ashton Research Station-Weather Generator

MAE:

Mean absolute error

NARX:

Nonlinear Autoregressive Network with Exogenous inputs

NCAR:

National Center for Atmospheric Research, USA

R :

Correlation coefficient

R 2 :

Coefficient of determination

RBF:

Radial basis function

RCP:

Representative concentration pathway

RMSE:

Root mean square error

RNN:

Recurrent Neural Network

SEE:

standard error estimates

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Acknowledgments

Special thanks go to the East Azerbaijan Regional Water Organization and Meteorological Organization for their contributions and preparing data used in present research work.

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Authors and Affiliations

  1. Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Esmaeil Jeihouni & Mirali Mohammadi

  2. Department of Civil Eng., Faculty of Eng., Urmia University, PO Box 165, Urmia, Iran

    Mirali Mohammadi

  3. Department of Water Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Saeid Eslamian

  4. Department of Water Resources Research, Water Research Institute (WRI), Ministry of Energy, Tehran, Iran

    Mohammad Javad Zareian

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  1. Esmaeil Jeihouni
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  2. Mirali Mohammadi
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  3. Saeid Eslamian
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Correspondence to Mirali Mohammadi.

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Jeihouni, E., Mohammadi, M., Eslamian, S. et al. Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study—Shabestar Plain, Iran. Environ Monit Assess 191, 620 (2019). https://doi.org/10.1007/s10661-019-7784-6

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