Skip to main content
Log in

Optimal Design of Groundwater Monitoring Network Using the Combined Election-Kriging Method

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Groundwater monitoring requires a great deal of cost and time that optimizing the quantitative groundwater monitoring network with selecting the optimal number of sampling wells and determining their optimal location for reducing the cost and time of quantitative groundwater assessment are necessary. The data from 110 observation wells of Neyshabur plain in Iran range from the year 1986 to 2016 were studied. The combined Election-Kriging method was used to analyze these data, and the Pareto chart was plotted to determine the optimal number and location in two scenarios. The first scenario was to determine the optimal wells location among the existing wells and the second scenario was to determine the optimal wells location for monitoring groundwater levels throughout the plain. To limit the search space, the maximum and minimum number of monitoring network wells were selected 30 and 85 respectively. Based on the results, the selected method accurately provided the appropriate location of wells, so that in the first scenario, the RMSE values ​​for the number of wells were in the range of 0.71 to 2.34 m. which are acceptable. In the second scenario, the RMSE values ​​of between 1.04 and 2.89 m were obtained, which are appropriate values ​​according to the objective of the problem. Also, the distribution of the wells selected in the area is also uniform in most numbers according to the second scenario, which indicates the good accuracy of the method.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  • Abou Zaki N, Torabi Haghighi A, Rossi M, Tourian P, Klove MJ B (2019) Monitoring Groundwater Storage Depletion Using Gravity Recovery and Climate Experiment (GRACE) Data in Bakhtegan Catchment. Iran Water 11(7):1456

  • Adiat KAN, Ajayi OF, Akinlalu AA, Tijani IB (2020) Prediction of groundwater 684 level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, 685 southwestern Nigeria. Appl Water Sci 10(1):8

    Article  Google Scholar 

  • Ahmadi SH, Sedghamiz A (2008) Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environ Monit Assess 138:357–368

    Article  Google Scholar 

  • Alizadeh Z, Mahjouri N (2017) A spatiotemporal Bayesian maximum entropy-based methodology for dealing with sparse data in revising groundwater quality monitoring networks: the Tehran region experience. Environ Earth Sci 76:436

    Article  Google Scholar 

  • Asefa T, Kemblowski MW, Urroz G, McKee M, Khalil A (2004) Support vectors-based groundwater head observation networks design. Water Resour Res 40:W11509

    Article  Google Scholar 

  • Ayvaz MT, Elçi A (2018) Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach. J Hydrol 563:1078–1091

    Article  Google Scholar 

  • Barzegar R, Fijani E, Moghaddam AA, Tziritis E (2017) Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Sci Total Environ 599:20–31

    Article  Google Scholar 

  • Bashi-Azghadi SN, Kerachian R (2010) Locating monitoring wells in groundwater systems using embedded optimization and simulation models. Sci Total Environ 408:2189–2198

    Article  Google Scholar 

  • Bhat S, Motz LH, Pathak C, Kuebler L (2015) Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA. Environ Monit Assess 187(1):1–15

    Google Scholar 

  • Chandan KS, Yashwant BK (2017) Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: a case study in Wainganga Sub-basin, India. Chin Geograph Sci 27(2):201–215

    Article  Google Scholar 

  • Chao Y, Qian H, Fang Y (2011) Optimum design of groundwater level monitoring network in Yinchuan plain. Water Resour Environ Protect 1:278–281

    Google Scholar 

  • Cressie N (1993) Statistics for Spatial Data. Wiley, New York

    Book  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 Chang 148:181–191

    Article  Google Scholar 

  • Emami H, Derakhshan F (2015) Election algorithm: A new socio-politically inspired strategy. AI Commun 28(3):591–603

    Article  Google Scholar 

  • Emami S, Choopan Y, Parsa J (2019) Modeling the groundwater level of the miandoab plain using artificial neural network method and election and genetic algorithms’. Iran J Ecohydrol 5(4):1175–1189 ((In Persian))

    Google Scholar 

  • Guo Y, Wang j, Yin X (2011) Optimizing the ground water monitoring network using MSN Theory. Procedia Soc Behav Sci 21:240–242

    Article  Google Scholar 

  • Herrera GS, Simuta-Champo R (2013) Optimal design of groundwater-quality sampling networks with three-dimensional selection of sampling locations using an ensemble smoother. J Water Resour Plan Manag 139:682–692

    Article  Google Scholar 

  • Hosseini M, Kerachian R (2017a) A Bayesian Maximum Entropy-based methodology for the optimal spatiotemporal design of groundwater monitoring networks. Environ Monit Assess 189(4):433

    Article  Google Scholar 

  • Hosseini M, Kerachian R (2017b) A data fusion-based methodology for optimal redesign of groundwater monitoring networks. J Hydrol 552:267–282

    Article  Google Scholar 

  • Izadi A, Abdalla O, Joodavi A, Chen M (2017) Groundwater Modeling and Sustainability of a Transboundary Hardrock–Alluvium Aquifer in North Oman Mountains. Water 9(3):161–169

  • Janardhanan S, Gladish D, Gonzalez D, Pagendam D, Pickett T, Cui T (2020) Optimal design and prediction-independent verification of groundwater monitoring network. Water 12:123

    Article  Google Scholar 

  • Khashei-siuki A, Sarbazi M (2015) Evaluation of ANFIS, ANN and geostatistic models to spatial distribution of groundwater qulity (case study: mashhad plain in iran). Arab J Geosci 8:903–991. Springer

  • Kouziokas GN, Chatzigeorgiou A, Perakis K (2018) Multilayer feed forward models in groundwater level forecasting using meteorological data in public management. Water Resour Manag 32(15):5041–5052

    Article  Google Scholar 

  • Kumar V, Ramadevi (2006) Kriging of groundwater levels-A case study. J Spat Hydrol 6(1):81–94

  • Mansouri Daneshvar MR, Ebrahimi M, Nejadsoleymani H (2019) An overview of climate change in Iran: facts and statistics. Environ Syst Res 8:7

    Article  Google Scholar 

  • Manzione RL, Wendland E, Tanikawa DH (2012) Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil. Hydrogeol J 20:1239–1249

    Article  Google Scholar 

  • Mazandaran Provincial Water Corporation (2015) Final report of groundwater monitoring network optimization using statistical land methods (case study of Ghaemshahr plain - Joybar - Mazandaran). https://research.wrm.ir//

  • Mirzaei Nodoushan F, Bozorg-Haddad O, Loaíciga HA (2017) Optimal design of groundwater-level monitoring networks. J Hydroinf 19(6):920–929

  • Nazeri Tahroudi M, Khashei Siuki A, Ramezani Y (2019) Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory. Environ Monit Assess 191:250

    Article  Google Scholar 

  • Pham TG, Kappas M, Van Huynh C, Nguyen LHK (2019) Application of ordinary kriging and regression kriging method for soil properties mapping in hilly region of Central Vietnam. ISPRS Int J Geo-Inf 8:2–17

  • Reed P, Kollat JB, Devireddy VK (2007) Using interactive archives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design. Environ Model Softw 22(5):683–692

    Article  Google Scholar 

  • Run Y, Li X, Ge Y, Lu X, Lian Y (2015) Optimal selection of groundwater level-monitoring sites in the Zhangye, Basin Northwest China. J Hydrol 205:209–215

    Article  Google Scholar 

  • Ruybal CJ, Hogue TS, McCray JE (2019) Evaluation of groundwater levels in the Arapahoe aquifer using spatiotemporal regression kriging. Water Resour Res 55:2820–2837

    Article  Google Scholar 

  • Sreekanth J, Lau H, Pagendam DE (2017) Design of optimal groundwater monitoring well network using stochastic modelling and reduced-rank spatial prediction. Water Resour Res 53:6821–6840

    Article  Google Scholar 

  • Varouchakis EA, Theodoridou PG, Karatzas P (2019) Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. J Hydrol 575:487–498

    Article  Google Scholar 

  • Water Research Institute (2015) Water Resources Research and Research Institute. http://www.wrr-wri.ir

  • Wunsch A, Liesch T, Broda S (2018) Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). J Hydrol 567:743–758

    Article  Google Scholar 

  • Yeh MS, Lin YP, Chang LC (2006) Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environ Geol 50(1):101–121

    Article  Google Scholar 

  • Yu H, Wen X, Feng Q, Deo RC, Si J, Wu M (2018) Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water Resour Manag 32(1):301–323

    Article  Google Scholar 

  • Zhou Y, Dong D, Liu J, Li W (2013) Upgrading a regional groundwater level monitoring network for Beijing Plain, China. Geosci Front 4(1):127–138

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abbas Khashei Siuki.

Ethics declarations

Conflict of Interest

We authors of the paper confirm that there is “Conflict Of Interest- None”.

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

Kavusi, M., Khashei Siuki, A. & Dastourani, M. Optimal Design of Groundwater Monitoring Network Using the Combined Election-Kriging Method. Water Resour Manage 34, 2503–2516 (2020). https://doi.org/10.1007/s11269-020-02568-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-020-02568-7

Keywords

Navigation