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MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

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Abstract

Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

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References

  • Barca E, Passarella G, Lo Presti R, Masciale R, Vurro M (2006a) HarmoniRiB River Basin data documentation. Chapter 7 – Candelaro River Basin D6.3, pp 1–47

  • Barca E, Passarella G, Lo Presti R, Masciale R, Vurro M (2006b) Candelaro River Basin, Italy—A HarmoniRiB case study. HarmoniRiB project deliverables D7.6, pp 1–54

  • Barca E, Passarella G, Uricchio V (2008) Optimal extension of the rain gauge monitoring network of the Apulian regional consortium for crop protection. Environ Monit Assess 145(1–3):375–386

    Article  Google Scholar 

  • Barca E, Passarella G, Vurro M, Morea, A (2011) A software for optimal information based downsizing/upsizing of existing monitoring networks. In: Cafarelli B (ed) Spatial data methods for environmental and ecological processes, 2nd edn, Cdp Service Edizioni

  • Barca E, Passarella G, Vurro M, Morea A (2013a) Optimal redesign of groundwater monitoring networks: a data-driven multi-approach software. In: 8th Int Conf EWRA: Water Resources Management in an Interdisciplinary and Changing Context, Porto (PT)

  • Barca E, Calzolari MC, Passarella G, Ungaro F (2013b) Predicting shallow water table depth at regional scale: optimizing monitoring network in space and time. Water Resour Manag. doi:10.1007/s11269-013-0461-6 (first published online)

    Google Scholar 

  • Bellman R (1954) The theory of dynamic programming. Bull Am Math Soc 60:503–515

    Article  Google Scholar 

  • Bellman R (1957) Dynamic programming. Princeton University Press, Princeton, Republished 2003, Dover. ISBN 0-486-42809-5

    Google Scholar 

  • Bertsekas DP (1995) Dynamic programming and optimal control, Vol 1, No 2. Athena Scientific, Belmont

    Google Scholar 

  • Bertsimas D, Tsitsiklis J (1993) Simulated annealing. Stat Sci 8(1):10–15

    Article  Google Scholar 

  • Castrignanò A, Morari F, Fiorentino C, Pagliarin C, Brenna S. (2008) Constrained optimization of spatial sampling in skeletal soils using EMI data and continuous simulated annealing, Proceeding of 1st Global Workshop on High Resolution Digital Soil Sensing & Mapping. 5–8 February 2008, Sydney – Australia (CD-ROM)

  • Christakos G, Killam BR (1993) Sampling design for classifying contaminant level using annealing search algorithms. Water Resour Res 29(12):4063–4076

    Article  Google Scholar 

  • Cressie NAC (1993) Statistics for spatial data. Wiley series in probability and mathematical statistics: applied probability and statistics. Wiley, New York

    Google Scholar 

  • Cressie N, Gotway CA, Grondona MO (1990) Spatial prediction from networks. Chemometr Intell Lab Syst 7(3):251–271

    Article  Google Scholar 

  • Cunha MDC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Resour Plan Manag 125(4):215–221

    Article  Google Scholar 

  • D’Agostino V, Passarella G, Vurro M (1997) Assessment of the optimal sampling arrangement based on cokriging estimation variance reduction approach. In: Congr International Association of Hydraulic Research, IAHR, pp 246–252

  • D’Agostino V, Greene EA, Passarella G, Vurro M (1998) Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization. Environ Geol 36(3/4):285–295

    Article  Google Scholar 

  • Delhomme JP (1978) Kriging in the hydrosciences. Adv Water Resour 1(5):251–266

    Article  Google Scholar 

  • Delhomme JP, Delfiner P (1973) Application du krigeage al ‘optimisation d’une campagne pluviometrique en zone aride’. In: Proc Symp Design of Water Resources Projects with Inadequate Data, Vol 2, pp 191–210

  • Deutsch CV, Cockerham PW (1994) Practical considerations in the application of simulated annealing to stochastic simulation. Math Geol 26:67–82

    Article  Google Scholar 

  • Di Zio S, Fontanella L, Ippoliti L (2004) Optimal spatial sampling schemes for environmental surveys. Environ Ecol Stat 11:397–414

    Article  Google Scholar 

  • Diggle P, Ribeiro P (2007) Model-based geostatistics. Springer, New York

    Google Scholar 

  • Drosou M, Pitoura E (2009) Comparing diversity heuristics. Technical Report 2009–05. Computer Science Department, University of Ioannina

  • Dutta D, Gupta AD, Ramnarong V (1998) Design and optimization of a groundwater monitoring system using GIS and multicriteria decision analysis. Groundw Monit Remediat 18(1):139–147

    Article  Google Scholar 

  • Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13(2):311–329

    Article  Google Scholar 

  • Henderson D, Jacobson SH, Johnson AW (2003) The theory and practice of simulated annealing. In: Glover FW, Kochenberger GA (eds) Handbook of metaheuristics. Springer, US, pp 287–319

    Google Scholar 

  • Hu MG, Wang JF (2011) A spatial sampling optimization package using MSN theory. Environ Model Software 26:546–548

    Article  Google Scholar 

  • Hughes JP, Lettenmaier DP (1981) Data requirements for kriging: estimation and network design. Water Resourc Res 17(6):1641–1650

    Article  Google Scholar 

  • Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press

  • Ishikawa H (2003) Exact optimization for Markov random fields with convex priors. IEEE Trans Pattern Anal Mach Intell 25(10):1333–1336

    Article  Google Scholar 

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic press

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  Google Scholar 

  • Lo Presti R, Barca E, Passarella G (2010) A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy). Environ Monit Assess 160(1–4):1–22

    Article  Google Scholar 

  • Lobato FS, Assis EG, Steffen Jr V, da Silva Neto AJ (2012) Design and identification problems of rotor bearing systems using the simulated annealing algorithm. In: Sales Guerra Tsuzuki M (ed) Simulated Annealing—single and multiple objective problems. ISBN: 978-953-51-0767-5, InTech. doi: 10.5772/47833

  • Mandelbrot BB (1982) The fractal geometry of nature. Freeman, New York

    Google Scholar 

  • Masciale R, Barca E, Passarella G (2011) A methodology for rapid assessment of the environmental status of the shallow aquifer of “Tavoliere di Puglia” (Southern Italy). Environ Monit Assess 177(1–4):245–261

    Article  Google Scholar 

  • Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  • Myers JC (1997) Geostatistical error management: quantifying uncertainty for environmental sampling and mapping. Wiley

  • Naoum S, Tsanis IK (2004) Integrating multicriteria analysis and GIS for assessing raingage worth within an established network. J Am Water Resourc Assoc 40(6):1449–1468

    Article  Google Scholar 

  • Nourani Y, Andresen B (1998) A comparison of simulated annealing cooling strategies. J Phys A: Math Gen 31(41):8373

    Article  Google Scholar 

  • Nunes LM, Cunha MC, Ribeiro L (2004) Groundwater monitoring network optimization with redundancy reduction. J Water Res Plan Manag 130(1):33–43

    Article  Google Scholar 

  • Nunes LM, Caeiro S, Cunha MC, Ribeiro L (2006) Optimal estuarine sediment monitoring network design with simulated annealing. J Environ Manag 78(3):294–304

    Article  Google Scholar 

  • Nunes LM, Paralta E, Cunha MC, Ribeiro L (2007) Comparison of variance‐reduction and space‐filling approaches for the design of environmental monitoring networks. Comput‐Aided Civ Infrastruct Eng 22(7):489–498

    Article  Google Scholar 

  • Ortner M, Descombes X, Zerubia J (2007) An adaptive simulated annealing cooling schedule for object detection in images. Rapport de recherche. RR n 6336

  • Pardo-Igùzquiza E (1998) Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. J Hydrol 210:206–220

    Article  Google Scholar 

  • Passarella G, Vurro M, D’Agostino V, Giuliano G, Barcelona MJ (2002) A probabilistic methodology to assess the risk of groundwater quality degradation. Environ Monit Assess 79(1):57–74

    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):1–21

    Article  Google Scholar 

  • Passarella G, Barca E, Lo Porto A (2006) Collection and elaboration of data for the pilot area of the Candelaro River (Italy). Development of completed database accompanied by appropriate software package for the retrieval, analysis and processing of flood related data. Description of the Candelaro Catchment. In: FloodMed project deliverables 2.2, pp 1–57

  • Rennen G (2009) Subset selection from large datasets for Kriging modelling. Struct Multidiscip Optim 38(6):545–569

    Article  Google Scholar 

  • Richey M (2010) The evolution of Markov Chain Monte Carlo methods. Am Math Mon 117(5):383–413

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Stevens DL Jr (1997) Variable density grid‐based sampling designs for continuous spatial populations. Environmetrics 8(3):167–195

    Article  Google Scholar 

  • Stevens DL Jr (2006) Spatial properties of design-based versus model-based approaches to environmental sampling. In: Caetano M, Painho M (eds) 7th Int Symp Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, p 119

  • Triki E, Collette Y, Siarry P (2005) A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. Eur J Oper Res 166(1):77–92

    Article  Google Scholar 

  • Tsitsiklis JN (1989) Markov chains with rare transitions and simulated annealing. Math Oper Res 14(1):70–90

    Article  Google Scholar 

  • Van Groenigen JW, Stein A (1998) Spatial simulated annealing for constrained optimisation of spatial sampling schemes. J Environ Qual 27:1078–1086

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Van Groenigen JW, Pieters G, Stein A (2000) Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics 11:227–244

    Article  Google Scholar 

  • Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Springer, The Netherlands, pp 7–15

    Book  Google Scholar 

  • Winkel H, Stein A (1997) Optimal cost-effective sampling for monitoring and dredging of contaminated sediments. J Environ Qual 26:933–946

    Article  Google Scholar 

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Acknowledgments

Water table data used in this paper have been collected within the project “TIZIANO” for the qualitative and quantitative monitoring of the regional groundwater bodies. Thanks are due to the Water Protection Department of the “Regione Puglia” who kindly provided the data.

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Authors and Affiliations

  1. CNR-IRSA, Water Research Institute—National Research Council, V.le De Blasio 5, 70132, Bari, Italy

    Emanuele Barca, Giuseppe Passarella & Michele Vurro

  2. Department of Physics, University of Bari, Via Amendola 173, 70126, Bari, Italy

    Alberto Morea

Authors
  1. Emanuele Barca
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  2. Giuseppe Passarella
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  3. Michele Vurro
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  4. Alberto Morea
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Correspondence to Emanuele Barca.

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Barca, E., Passarella, G., Vurro, M. et al. MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks. Water Resour Manage 29, 619–644 (2015). https://doi.org/10.1007/s11269-014-0859-9

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  • DOI: https://doi.org/10.1007/s11269-014-0859-9

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