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Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran

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Abstract

In recent decades, increasing global water demand, coupled with the effects of climate change, has led to increased variation in groundwater level depletion. In this work, the effect of climate parameters is investigated with respect to groundwater levels in the Shabestar Plain, Iran. In the first step, the best models for the study region were selected from the general circulation models provided under the Fifth Assessment Report of the United Nations Intergovernmental Panel on Climate Change. To increase the spatial resolution of the precipitation data, downscaling of the models was performed using the Long Ashton Research Station weather generator for three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP8.5) for the future period 2020–2049. The results of these models illustrated an increase in temperature and a decrease in precipitation for the study region. In the next step, an artificial neural network (ANN) technique for studying aquifer behavior was used. To increase the efficiency of the model, spatial and temporal preprocessing of data was performed using k-means clustering and wavelet transform de-noising, respectively. A fuzzy inference system was also used as a tool for estimating groundwater extraction and reducing uncertainty of illegal extraction. The results of ANN for five selected observation wells showed correlation coefficients of 0.92, 0.86, 0.76, 0.57 and 0.94 for the simulation. The model simulation under the three above-mentioned scenarios and the trend in groundwater decline in the Shabestar Plain for the base and future periods illustrated that the groundwater level dynamics were not related solely to climate parameters and that the impact of anthropogenic factors would be high.

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(adopted from Barzegar et al. 2017)

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References

  • Asghari MA, Mohammadi A (2003) Sources of salinity in groundwater of Shabestar plain aquifers. J Agric Sci (Univ Tabriz) 13(3):69–78

    Google Scholar 

  • Banerjee P, Singh V, Chatttopadhyay K, Chandra P, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3):212–220

    Article  Google Scholar 

  • Barzegar R, Moghaddam AA, Soltani S, Fijani E, Tziritis E, Kazemian N (2017) Heavy metal(loid)s in the groundwater of Shabestar area (Nw Iran): source identification and health risk assessment. Expo Health. https://doi.org/10.1007/s12403-017-0267-5

    Article  Google Scholar 

  • Bashi-Azghadi SN, Kerachian R, Bazargan-Lari MR, Solouki K (2010) Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. Expert Syst Appl 37(10):7154–7161

    Article  Google Scholar 

  • Bear J, Cheng AHD (2010) Modeling groundwater flow and contaminant transport. Springer, New York

    Book  Google Scholar 

  • Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth Parts A/B/C 31(18):1164–1171

    Article  Google Scholar 

  • Chang J, Wang G, Mao T (2015) Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model. J Hydrol 529:1211–1220

    Article  Google Scholar 

  • Chang F-J, Chang L-C, Huang C-W, Kao I-F (2016) Prediction of monthly regional groundwater levels through hybrid soft-computing techniques. J Hydrol 541:965–976

    Article  Google Scholar 

  • Chaudhari S, Felfelani F, Shin S, Pokhrel Y (2018) Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century. J Hydrol 560:342–353

    Article  Google Scholar 

  • Choubin B, Malekian A (2017) Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ Earth Sci 76(15):538

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896

    Article  Google Scholar 

  • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240

    Article  Google Scholar 

  • De Graaf IEM (2016) Limits to global groundwater consumption: effects on groundwater levels and river low flows. Utrecht University, Utrecht. ISBN 978-90-6266-418-4

    Google Scholar 

  • Ebrahimi H, Rajaee T (2017) Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Glob Planet Change 148:181–191

    Article  Google Scholar 

  • Eslamian S (2014) Handbook of engineering hydrology: vol 2: modeling, climate change, and variability. CRC Press, Boca Raton

    Book  Google Scholar 

  • Foddis ML, Ackerer P, Montisci A, Uras G (2013) Polluted aquifer inverse problem solution using artificial neural networks. AQUA Mundi 4:15–21

    Google Scholar 

  • Foddis ML, Ackerer P, Montisci A, Uras G (2015) ANN-based approach for the estimation of aquifer pollutant source behaviour. Water Sci Technol Water Supply 15(6):1285–1294

    Article  Google Scholar 

  • Ghose DK, Panda SS, Swain PC (2010) Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. J Hydrol 394(3–4):296–304

    Article  Google Scholar 

  • Green TR, Taniguchi M, Kooi H, Gurdak JJ, Allen DM, Hiscock KM, Treidel H, Aureli A (2011) Beneath the surface of global change: impacts of climate change on groundwater. J Hydrol 405(3):532–560

    Article  Google Scholar 

  • IPCC (2014) Fifth assessment report (AR5): Climate change 2013: 2014/climate change 2014: impacts, adaptation, and vulnerability; Part B. Cambridge University Press, Cambridge

    Google Scholar 

  • Kabiri R (2014) Assessment of climate change impact on runoff and peak flow: a case study on Klang watershed in West Malaysia. University of Nottingham, Nottingham

    Google Scholar 

  • Li Z, Mao X-Z (2011) Global multiquadric collocation method for groundwater contaminant source identification. Environ Modell Softw 26(12):1611–1621

    Article  Google Scholar 

  • Li X, Tsai FTC (2009) Bayesian model averaging for groundwater head prediction and uncertainty analysis using multimodel and multimethod. Water Resour Res 45(9):W09403

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    Article  Google Scholar 

  • McCallum J, Crosbie R, Walker G, Dawes W (2010) Impacts of climate change on groundwater in Australia: a sensitivity analysis of recharge. Hydrogeol J 18(7):1625–1638

    Article  Google Scholar 

  • McCuen RH (2016) Modeling hydrologic change: statistical methods. CRC Press, Boca Raton

    Book  Google Scholar 

  • Mohamed A, Hawas Y (2004) Neuro-fuzzy logic model for evaluating water content of sandy soils. Comput Aided Civ Infrastruct Eng 19(4):274–287

    Article  Google Scholar 

  • Nason GP, Von Sachs R (1999) Wavelets in time-series analysis. Philos Trans R Soc Lond A Math Phys Eng Sci 357(1760):2511–2526

    Article  Google Scholar 

  • Nourani V (2015) Basics of hydroinformatics, in Farsi. Tabriz University Press, Tabriz

    Google Scholar 

  • Nourani V, Andalib G (2015) Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. J Mt Sci 12(1):85–100

    Article  Google Scholar 

  • Nourani V, Mousavi S (2016) Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method. J Hydrol 536:10–25

    Article  Google Scholar 

  • Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22(26):5054–5066

    Article  Google Scholar 

  • Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manag 23(14):2877

    Article  Google Scholar 

  • Nourani V, Baghanam AH, Rahimi AY, Nejad FH (2014) Evaluation of wavelet-based de-noising approach in hydrological models linked to artificial neural networks. Comput Intell Tech Earth Environ Sci. https://doi.org/10.1007/978-94-017-8642-3_12

    Article  Google Scholar 

  • Nourani V, Alami MT, Vousoughi FD (2015) Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 524:255–269

    Article  Google Scholar 

  • Nourani V, Mousavi S, Dabrowska D, Sadikoglu F (2017) Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media. J Hydrol 548:569–587

    Article  Google Scholar 

  • Rajaee T, Nourani V, Pouraslan F (2016) Groundwater level forecasting using wavelet and kriging. J Hydraul Struct 2(2):1–21

    Google Scholar 

  • Shiri J, Kisi O, Yoon H, Lee K-K, Nazemi AH (2013) Predicting groundwater level fluctuations with meteorological effect implications A comparative study among soft computing techniques. Comput Geosci 56:32–44

    Article  Google Scholar 

  • Singh RM, Datta B (2007) Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data. Water Resour Manag 21(3):557–572

    Article  Google Scholar 

  • Singh RM, Datta B, Jain A (2004) Identification of unknown groundwater pollution sources using artificial neural networks. J Water Resour Plan Manag 130(6):506–514

    Article  Google Scholar 

  • Sreekanth P, Sreedevi P, Ahmed S, Geethanjali N (2011) Comparison of FFNN and ANFIS models for estimating groundwater level. Environ Earth Sci 62(6):1301–1310

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132

    Article  Google Scholar 

  • Taormina R, Chau K-W (2015) Neural network river forecasting with multi-objective fully informed particle swarm optimization. J Hydroinform 17(1):99–113

    Article  Google Scholar 

  • Tapoglou E, Karatzas GP, Trichakis IC, Varouchakis EA (2014) A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation. J Hydrol 519:3193–3203

    Article  Google Scholar 

  • Treidel H, Martin-Bordes JL, Gurdak JJ (2011) Climate change effects on groundwater resources: a global synthesis of findings and recommendations. CRC Press, Boca Raton

    Book  Google Scholar 

  • Wada Y (2013) Human and climate impacts on global water resources. Utrecht University, Utrecht. ISBN 978-90-6266-346-0

    Google Scholar 

  • Yoon H, Jun S-C, Hyun Y, Bae G-O, Lee K-K (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138

    Article  Google Scholar 

  • Zamanirad M, Sedghi H, Sarraf A, Saremi A, Rezaee P (2018) Potential impacts of climate change on groundwater levels on the Kerdi-Shirazi plain, Iran. Environ Earth Sci 77(11):415

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their deepest thanks to the East Azerbaijan Regional Water Organization and Meteorological Organization for their contributions as well as preparation of data used in this research.

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Authors and Affiliations

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

    Esmaeil Jeihouni & Saeid Eslamian

  2. Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran

    Saeid Eslamian

  3. Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran

    Mirali Mohammadi

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

    Mohammad Javad Zareian

Authors
  1. Esmaeil Jeihouni
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  2. Saeid Eslamian
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  3. Mirali Mohammadi
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  4. Mohammad Javad Zareian
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Correspondence to Saeid Eslamian.

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Jeihouni, E., Eslamian, S., Mohammadi, M. et al. Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran. Environ Earth Sci 78, 293 (2019). https://doi.org/10.1007/s12665-019-8283-3

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