Skip to main content

Advertisement

SpringerLink
Log in

Improvement of water table interpolation and groundwater storage volume using fuzzy computations

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The water table is an important piece of data for hydrogeological studies, particularly as input data to groundwater simulation models. Since the accuracy of groundwater simulation models significantly depends on input data, this study highlights the application of fuzzy kriging to improve the accuracy of water table interpolation. The results of the fuzzy kriging approach are compared with common methods in water table interpolation like ordinary kriging, inverse distance weighting (IDW), and Thiessen polygon methods to justify the suitability of the fuzzy kriging. The Gilan and Zanjan plains, located in the northwest of Iran, are used as case study areas. The Gilan Plain is characterized by a dense and regular piezometric network and gentle hydraulic gradient. The longitudinal plain of Zanjan has a sparse and irregular piezometric network and steep hydraulic gradient. Since these plains have different piezometric network configurations, the sensitivity of the interpolation methods to the monitoring point configuration is analyzed. The cross-validation method is employed to validate the accuracy of interpolation methods in water table interpolation. In control points, the average of root-mean-square errors associated with groundwater water table values estimated using fuzzy kriging, ordinary kriging, IDW, and Thiessen polygon methods are obtained to be respectively 1.36, 1.93, 3.49, and 9.10 in the Gilan Plain and 13.60, 22.86, 32.30, and 59.81 in the Zanjan Plain. The results indicate that the fuzzy kriging technique has greater precision in comparison with other methods, especially under the conditions of the sparse piezometric network and steep hydraulic gradient. The results also demonstrate that the used methods generally have higher accuracy in the Gilan Plain with a regular piezometric network than in the Zanjan Plain. Furthermore, Thiessen polygon, IDW, and ordinary kriging methods overestimated water table in comparison with the fuzzy kriging method in our cases. This overestimation may cause large error values in subsequent calculations such as water budget and aquifer storage which play a major role in the appropriate management of water resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7.

Similar content being viewed by others

References

  • Adhikary, P. P., & Dash, C. J. (2017). Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth. Applied Water Science, 7(1), 339–348.

    Article  Google Scholar 

  • Ahmadi, S. H., & Sedghamiz, A. (2007). Geostatistical analysis of spatial and temporal variations of groundwater level. Journal of Environmental Monitoring Assessment, 129, 277–294. https://doi.org/10.1007/s10661-006-9361-z.

    Article  Google Scholar 

  • Alley, W. M. (1993). Regional ground-water quality (1st ed.p. 634). New York, NY: International Thomson publishing.

    Google Scholar 

  • Arslan, H. (2014). Estimation of spatial distribution of groundwater level and risky areas of seawater intrusion on the coastal region in Çarşamba Plain, Turkey, using different interpolation methods. Environmental monitoring and assessment, 186(8), 5123–5134.

    Article  Google Scholar 

  • Ayvaz, M. T., & Elci, A. (2017). Seeking the optimum groundwater monitoring network using a genetic algorithm approach. Kuala Lumpur, Malaysia: Proceedings of the 37th IAHR World Congress, August 13 – 18, 2017.

    Google Scholar 

  • Bardossy, A., Bogardi, I., & Kelly, W. E. (1989). Geostatistics utilizing imprecise (fuzzy) information. Fuzzy Sets and Systems, 31, 311–327.

    Article  Google Scholar 

  • Bardossy, A., Bogardi, I., & Kelly, W. E. (1990). Kriging with imprecise (Fuzzy) variograms. Mathematical Geology, 22(4), 81–94.

    Article  Google Scholar 

  • Brassington, R. (2017). Field hydrogeolog y (geological field guide), 4th Edition, John Wiley & Sons.

  • Caha, J., Marek, L., & Dvorský, J. (2015). Predicting PM10 concentrations using fuzzy kriging. In E. Onieva, I. Santos, E. Osaba, H. Quintián, & E. Corchado (Eds.), Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science (Vol. 9121). Cham: Springer.

    Google Scholar 

  • Dash, J., Sarangi, A., & Singh, D. (2010). Spatial variability of groundwater depth and quality parameters in the national capital territory of Delhi. Environmental Management, 45(3), 640–650.

    Article  CAS  Google Scholar 

  • Dhar, A., & Patil, R. S. (2011). Fuzzy uncertainty based design of groundwater quality monitoring networks. Journal of Environmental Research And Development Vol, 5(3A).

  • Dixon, B. (2005). Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrology, 309, 17–38.

    Article  Google Scholar 

  • Dixon, B., & Uddameri, V. (2016). GIS and geocomputation for water resource science and engineering. UK: Jhon Wiley & Sons Ltd..

    Google Scholar 

  • Dou, C. H., Woldt, W., Dahab, M., & Bogardi, I. (1997). Transient groundwater flow simulation using a fuzzy set approach. Groundwater, 35(2), 205–215. https://doi.org/10.1111/j.1745-6584.1997.tb00076.x.

    Article  CAS  Google Scholar 

  • Du, X., Lu, X., Hou, J., & Ye, X. (2018). Improving the reliability of numerical groundwater modeling in a data-sparse region. Water, 10(3), 289.

    Article  Google Scholar 

  • Gilan Regional Water Authority (2014). Quality and quantity of water resources in the Astaneh-Kuchesfahan Region, Gilan Province. Iran Water Resources Management Company, Iran Ministry of Energy, Tehran, Iran (In Farsi). http://www.glrw.ir/SC.php?type=static&id=115.

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation (1st ed.). New York: Oxford University Press.

    Google Scholar 

  • Jalut, Q. H., Khalaf, R. M., & Abdul-Mehdi, T. R. (2013). Modeling of transient groundwater flow using fuzzy approach. Modern Applied Science, 7(4), 77.

    Article  Google Scholar 

  • Keum, J., Kornelsen, K., Leach, J., & Coulibaly, P. (2017). Entropy applications to water monitoring network design: a review. Entropy, 19(11), 613.

    Article  Google Scholar 

  • Kholghi, M., & Hosseini, S. M. (2009). Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environmental Modeling & Assessment, 14(6), 729–737.

    Article  Google Scholar 

  • Kresic, N. (1998). Quantitative Solutions in Hydrogeology and Groundwater Modeling (p. 461). USA: CRC press.

    Google Scholar 

  • Kumar, V. (2007). Optimal contour mapping of groundwater levels using universal kriging—a case study. Hydrological Sciences Journal, 52(5), 1038–1050.

    Article  Google Scholar 

  • Langrudi, M. A. O., Siuki, A. K., Javadi, S., & Hashemi, S. R. (2016). Evaluation of vulnerability of aquifers by improved fuzzy drastic method: case study: Aastane Kochesfahan plain in Iran. Ain Shams Engineering Journal, 7(1), 11–20.

    Article  Google Scholar 

  • Lee, K. H. (2005). First course on fuzzy theory and applications. Berlin, Germany: Springer.

    Google Scholar 

  • Lodwick, W. (2008). Fuzzy surfaces in GIS and geographical analysis. New York, USA: CRC press, Taylor & Francis.

    Google Scholar 

  • Masoomi, Z., Mesgari, M. S., & Menhaj, M. B. (2011). Modeling uncertainties in sodium spatial dispersion using a computational intelligence-based kriging method. Computers and Geosciences, 37(10), 1545–1554.

    Article  CAS  Google Scholar 

  • Meijerink, A. M., Bannert, D., Batelaan, O., Lubczynski, M. W., & Pointet, T. (2007). Remote sensing applications to groundwater. Programme and meeting document, Unesco,  Document code: SC.2007/WS/43, IHP/2007/GW/16, ​311 p.

  • Mirzaie-Nodoushan, f., Bozorg-Haddad, O., & Loaiciga, H. A. (2017). Optimal design of groundwater-level monitoring networks. Journal of hydroinformatics, 19(6), 920–929.

    Article  Google Scholar 

  • Olea, R. & Davis, J.C. (1999). Optimizing the high plains aquifer water-level observation network. Open File Report 15, 1999, Kansas Geological Survey, 1930 Constant Ave., Lawrence, KS 66047-3724.

  • Panahi, M., Misagi, F., & Asgari, P. (2018). Simulation and estimate of groundwater level fluctuations using GMS (case study: Zanjan plain). Environmental Sciences, 16(1), 1–14.

    Google Scholar 

  • Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: analysis and design. USA: Massachusetts Institute of technology publication.

    Book  Google Scholar 

  • Peeters, L., Fasbender, D., Batelaan, O., & Dassargues, A. (2010). Bayesian data fusion for water table interpolation: incorporating a hydrogeological conceptual model in kriging. Water Resources Research, 46(8).

  • Peterson, T. J., & Western, A. W. (2018). Statistical interpolation of groundwater hydrographs. Water Resources Research, 54(7), 4663–4680.

    Article  Google Scholar 

  • Piotrowski, J. A., Bartels, F., Salski, A., & Schmidt, G. (1996). Estimation of hydrogeological parameters for groundwater modelling with fuzzy geostatistics: closer to nature? IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 237, 511–522.

    Google Scholar 

  • Rei-Ab Consulting. (2009). Geology, soil, hydrogeology and hydrochemistry report for the Zanjan Plain, Iran. Tehran, Iran: Department of Environment (In Farsi).

    Google Scholar 

  • Rezaei, A., & Mohammadi, Z. (2017). Annual safe groundwater yield in a semiarid basin using combination of water balance equation and water table fluctuation. Journal of African Earth Sciences, 134, 241–248.

    Article  Google Scholar 

  • Ripley, B. D. (2004). Spatial Statistics. Oxford: WILEY publication.

    Google Scholar 

  • Rivest, M., Marcotte, D., & Pasquier, P. (2008). Hydraulic head field estimation using kriging with an external drift: a way to consider conceptual model information. Journal of Hydrology (Amsterdam), 361, 349–361. https://doi.org/10.1016/j.jhydrol.2008.08.006.

    Article  Google Scholar 

  • Rouhani, S. H., Srivastava, R. M., Desbarats, A. J., Cromer, M. V., & Johnson, A. I. (1996). Geostatistics for environmental and geotechnical applications. In ASTM committee. USA: Arizona.

    Google Scholar 

  • Sağir, Ç., & Kurtuluş, B. (2017). Hydraulic head and groundwater 111 Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS). Water SA, 43(3), 509–519.

    Article  Google Scholar 

  • Soltani-Mohammadi, S. (2016). FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information. Earth Science Informatics, 9(2), 235-245.

  • Taany, R., Tahboub, A., & Saffarini, G. (2009). Geostatistical analysis of spatiotemporal variability of groundwater level fluctuations in Amman-Zarqa basin, Jordan: A case study. Environ. Geol., 57(3), 525–535. https://doi.org/10.1007/s00254-008-1322-0.

    Article  Google Scholar 

  • Tapoglou, E., Karatzas, G. P., Trichakis, I. C., & Varouchakis, E. A. (2014). A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation. Journal of hydrology, 519, 3193–3203.

    Article  Google Scholar 

  • Theodossiou, N., & Latinopoulos, P. (2006). Evaluation and optimisation of groundwater observation networks using the kriging methodology. Environmental Modelling & Software, 21(7), 991–1000.

    Article  Google Scholar 

  • Tsanis, I. K., & Gad, M. A. (2001). A GIS precipitation method analysis of storm kinematics. Environmental Modelling and Software, 16(3), 273–281.

    Article  Google Scholar 

  • Varouchakis, Ε. A., & Hristopulos, D. T. (2013). Comparison of stochastic and deterministic methods for mapping groundwater level spatial variability in sparsely monitored basins. Environmental monitoring and assessment, 185(1), 1–19.

    Article  Google Scholar 

  • Waller, L. A., & Gotway, C. A. (2004). Applied spatial statistics for public health data. New Jersey, USA: John Wiley and sons.

    Book  Google Scholar 

  • Woldt, W., Dou, C., Bogardi, I., & Dahab, M. (1995). Using fuzzy set methods to consider parameter imprecision in groundwater flow models. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 227, 203–212.

    Google Scholar 

  • Xiao, Y., Gu, X., Yin, S., Shao, J., Cui, Y., Zhang, Q., & Niu, Y. (2016). Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus, 5(1), 425.

    Article  Google Scholar 

  • Zimmermann, H. J. (2001). Fuzzy sets theory and its applications (4th ed.). London: Kluwer academic publishers.

    Book  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Gilan and Zanjan regional water authorities for providing monthly groundwater level data.

Author information

Authors and Affiliations

  1. Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences, No. 444, Prof. Yousef Sobouti Blvd, P. O. Box 45195-1159, Zanjan, Iran

    Zohreh Masoumi & Abolfazl Rezaei

  2. Center for Research in Climate Change and Global Warming (CRCC), Zanjan, Iran

    Zohreh Masoumi & Abolfazl Rezaei

  3. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Jamshid Maleki

Authors
  1. Zohreh Masoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abolfazl Rezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jamshid Maleki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zohreh Masoumi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masoumi, Z., Rezaei, A. & Maleki, J. Improvement of water table interpolation and groundwater storage volume using fuzzy computations. Environ Monit Assess 191, 401 (2019). https://doi.org/10.1007/s10661-019-7513-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-7513-1

Keywords

Search

Navigation