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Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms

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Environmental Geology

Abstract

The optimal selection of monitoring wells is a major task in designing an information-effective groundwater quality monitoring network which can provide sufficient and not redundant information of monitoring variables for delineating spatial distribution or variations of monitoring variables. This study develops a design approach for an optimal multivariate geostatistical groundwater quality network by proposing a network system to identify groundwater quality spatial variations by using factorial kriging with genetic algorithm. The proposed approach is applied in designing a groundwater quality monitoring network for nine variables (EC, TDS, Cl−, Na, Ca, Mg, SO 2−4 , Mn and Fe) in the Pingtung Plain in Taiwan. The spatial structure results show that the variograms and cross-variograms of the nine variables can be modeled in two spatial structures: a Gaussian model with ranges 28.5 km and a spherical model with 40 km for short and long spatial scale variations, respectively. Moreover, the nine variables can be grouped into two major components for both short and long scales. The proposed optimal monitoring design model successfully obtains different optimal network systems for delineating spatial variations of the nine groundwater quality variables by using 20, 25 and 30 monitoring wells in both short scale (28.5 km) and long scale (40 km). Finally, the study confirms that the proposed model can design an optimal groundwater monitoring network that not only considers multiple groundwater quality variables but also monitors variations of monitoring variables at various spatial scales in the study area.

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References

  • Al-Zahrani MA, Moied K (2003) Optimizing water quality monitoring stations using genetic algorithms. Arab J Sci Eng 28(1B):57–75

    Google Scholar 

  • Batista AC, Sousa AJ, Batista MJ, Viegas L (2001) Factorial kriging with external drift: a cause study on the Penedono Region, Portugal. Appl Geochem 16(7–8):921–929

    Article  Google Scholar 

  • Benjemaa F, Mario MA, Loaiciga HA (1994) Multivariate geostatistical design of groundwater monitoring networks. J Water Resour Plan Manag ASCE 120(4):505–522

    Article  Google Scholar 

  • Bocchi S, Castrignano A, Fornaro F, Maggiore T (2000) Application of factorial kriging for mapping soil variation at field scale. Eur J Agron 13:295–308

    Article  Google Scholar 

  • Brus DJ, Spatjens LEEM, de Gruijter JJ (1999) A sampling scheme for estimating the mean extractable phosphorus concentration of fields for environmental regulation. Goderma 89:129–148

    Article  Google Scholar 

  • Cameron K, Hunter P (2002) Using spatial models and kriging techniques to optimize long-term groundwater monitoring networks: a case study. Environmetrics 13(5–6):629–656

    Article  Google Scholar 

  • Castrignano A, Giugliarini L, Risaliti R, Martinelli N (2000a) Study of spatial relationships amon some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma 97:39–60

    Article  Google Scholar 

  • Castrignano A, Goovaerts P, Lulli L, Bragato G (2000b) A geostatistical approach to estimate probability of occurrence of Tuber melanosporum in relation to some soil properties. Geoderma 98:95–113

    Article  Google Scholar 

  • Chang LC, Hsiao CT (2002) Dynamic optimal groundwater remediation including fixed and operation costs. Ground Water 40(5):481–490

    Article  Google Scholar 

  • Christakos G, Olea R(1988) A multiple-objective optimal exploration strategy. Math Comput Model 11:413–418

    Article  Google Scholar 

  • Cieniawski SE, Eheart JW, Ranjithan S (1995) Using genetic algorithm to solve a multiobjective groundwater monitoring problem. Water Resour Res 31(2):399–409

    Article  Google Scholar 

  • Deutsch CV, Journel AG (1992) Geostatistical software library and user’s guide. Oxford University Press, New York

    Google Scholar 

  • Dobermann A, Goovaerts P, George T (1995) Sources of soil variation in an acid Ultisol of the Philippines. Geoderma 68:173–191

    Article  Google Scholar 

  • Einax JW, Soldt U (1998) Multivariate geostatistical analysis of soil contaminations. Fresen J Anal Chem 361:10–14

    Article  Google Scholar 

  • Ferreyra RA, Apezteguia HP, Rereno R, Jones JW (2002) Reduction of soil water spatial sampling density using scaled semivariograms and simulated annealing. Geoderma 110:265–289

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithm in search, optimization, and machine learning. Addison-Wesley, Reading

    Google Scholar 

  • Goovaerts P (1992) Factorial kriging analysis: a useful tool for exploring the structure of multivariate spatial soil information. J Soil Sci 43:597–619

    Article  Google Scholar 

  • Goovaerts P (1994) Study of spatial relationships between two sets of variables using multivariate geostatistics. Geoderma 62:93–107

    Article  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resource evaluation. Oxford University Press, Oxford

    Google Scholar 

  • Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Bio Fertil Soils 27:315–334

    Article  Google Scholar 

  • Goovaerts P, Webster R (1994) Scale-dependent correlation between topsoil copper and cobalt concentrations in Scotland. Eur J Soil Sci 45(1):79–95

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in Natural and Artificial Systems, 2nd edn. Mass Inst Technol, Cambridge

    Google Scholar 

  • Hsiao CT, Chang LC (2002) Dynamic Optimal Groundwater Management with Inclusion of Fixed Costs. J Water Resource Plan Manag ASCE 128(1):57–65

    Article  Google Scholar 

  • Hudak PF, Loaiciga HA (1993) An optimization method for monitoring network design in multilayered groundwater flow systems. Water Resource Res 29(8):2835–2845

    Article  Google Scholar 

  • Jiménez-Espinosa R, Chica-Olmo M (1999) Application of geostatistics to identify gold-rich areas in the Finisterre-Fervenza region, NW Spain. Appl Geochem 14(1):133–145

    Article  Google Scholar 

  • Lark RM (2000) Design sampling grids from imprecise information on soil variability, and approach based on the fuzzy kriging variance. Geoderma 98:35–59

    Article  Google Scholar 

  • Lark RM (2002) Optimized spatial sampling of soil for estimation of the variogram by maximum likelihhod. Geoderma 105:49–80

    Article  Google Scholar 

  • Lin YP (2002) Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals. Environ Geol 42:1–10

    Article  Google Scholar 

  • Lin YP, Rouhani S (2001) Multiple-point variance analysis for optimal adjustment of a monitoring network. Environ Monit Assess 69(3):239–266

    Article  Google Scholar 

  • Lin YP, Chang TK, Shih CW, Tseng CH (2002) Factorial and indicator kriging with geographic information system to delineate spatial variations and pollution sources of soil heavy metals. Environ Geol 42:900–909

    Article  Google Scholar 

  • Loaiciga HA (1989) An optimization approach for groundwater quality monitoring network design. Water Resour Res 25(8):1771–1782

    Article  Google Scholar 

  • McKinney DC, Lin D (1994) Genetic algorithm solution of groundwater management models. Water Resour Res 30(6):1897–1906

    Article  Google Scholar 

  • Mitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge

    Google Scholar 

  • Mohan S (1997) Parameter estimation of nonlinear muskingum models using genetic algorithm. J Hydraul Eng 123(2):137–142

    Article  Google Scholar 

  • Pannatier Y (1996) Variowin: software for spatial data analysis in 2D. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Pardo-Iquzquiza E, Dowd PA (2002) FACTOR2D: a computer program for factorial cokriging. Comput Geosci 28:857–875

    Article  Google Scholar 

  • Passarella G, Vurro M, D’Agostino V, Barcelona MJ (2003) Cokriging optimization of monitoring network configuration based on fuzzy and non-fuzzy variogram evaluation. Environ Monit Assess 82:1–21

    Article  Google Scholar 

  • Pesti G, Kelly WE, Bogardi I (1994) Observation network design for selecting locations for water-supply wells. Environmetrics 5(2):91–110

    Article  Google Scholar 

  • Prakash MR, Sihgn VS (2000) Network design for groundwater monitoring—a case study. Environ Geol 39(6):628–632

    Article  Google Scholar 

  • Reed P, Minsker B, Valocchi AJ (2000) Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resource Res 36 (12):3731–3741

    Article  Google Scholar 

  • Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resource Res 30(2):457–481

    Article  Google Scholar 

  • Rouhani S (1985) Variance reduction analysis. Water Resource Res 21(6):837–846

    Article  Google Scholar 

  • Rouhani S, Hall TJ (1988) Geostatistical schemes for groundwater sampling. J Hydrol 103:85–102

    Article  Google Scholar 

  • Rouhani S, Wackernagel H, (1990) Multivariate geostatistical approach to space-time data analysis. Water Resource Res 26:585–591

    Article  Google Scholar 

  • Van Groenigen JW, Siderius W, Stein A (1999) Constrained optimal of soil sampling for minimization of the kriging variance. Geoderma 87:239–259

    Article  Google Scholar 

  • Wackernagel H (1994) Cokriging versus kriging in regionalized multivariate data analysis. Geoderma 62:83–92

    Article  Google Scholar 

  • Wackernagel H (1995) Multivariate geostatistics: an introduction with applications. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resource Res 27(9):2467–2471

    Article  Google Scholar 

  • Wang XJ, Qi F (1998) The effects of sampling design on spatial structure analysis of contaminated soil. Sci Total Environ 224:29–41

    Article  Google Scholar 

  • Wardlaw R, Sharif M(1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resource Plan Manag ASCE 125(1):25–33

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Civil Engineering, National Chiao Tung University, Hsinchu, 30010, Taiwan

    Ming-Sheng Yeh & Liang-Cheng Chang

  2. Department of Bioenvironmental Systems Engineering, National Taiwan University, 1 Section 4 Roosevelt Road, Taipei, 10617, Taiwan

    Yu-Pin Lin

Authors
  1. Ming-Sheng Yeh
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  2. Yu-Pin Lin
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  3. Liang-Cheng Chang
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Correspondence to Yu-Pin Lin.

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Yeh, MS., Lin, YP. & Chang, LC. Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environ Geol 50, 101–121 (2006). https://doi.org/10.1007/s00254-006-0190-8

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  • DOI: https://doi.org/10.1007/s00254-006-0190-8

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