SEARCH

SEARCH BY CITATION

References

  • Ainslie, B., C. Reuten, D. G. Steyn, N. D. Le, and J. V. Zidek (2009), Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants, J. Environ. Manag., 90, 27152729.
  • Alameddine, I., S. S. Qian, and K. H. Reckhow (2011), A Bayesian changepoint-threshold model to examine the effect of TMDL implementation on the flow-nitrogen concentration relationship in the Neuse River basin, Water Res., 45, 5162.
  • Alfonso, L., A. Lobbrecht, and R. Price (2010a), Information theory-based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46, W03528.
  • Alfonso, L., A. Lobbrecht, and R. Price (2010b), Optimization of water level monitoring network in polder systems using information theory, Water Resour. Res., 46, W12553.
  • Banerjee, S., A. E. Gelfand, and B. P. Carlin (2003), Hierarchical Modeling and Analysis for Spatial Data, Chapman and Hall/CRC, Boca Raton, Fla.
  • Barzilai, J., W. D. Cook, and B. Golany (1987), Consistent weights for judgements matrices of the relative importance of alternatives, Oper. Res. Lett., 6, 131134.
  • Bayarri, M. J. and J. O. Berger (2004), The interplay of Bayesian and frequentist analysis, Stat. Sci., 19, 5880.
  • Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis, 2nd ed., Springer-Verlag, New York.
  • Berthouex, M. P. and L. C. Brown (2002), Statistics for Environmental Engineers, CRC, Boca Raton, Florida.
  • Borsuk, M. E. (2001), A graphical probability network model to support water quality decision making for the Neuse River Estuary, 267 pp, Duke University, Durham, North Carolina.
  • Borsuk, M. E., C. A. Stow, R. A. Luettich, H. W. Paerl, and J. L. Pinckney (2001), Modelling oxygen dynamics in an intermittently stratified estuary: Estimation of process rates using field data, Estuar. Coast. Shelf Sci., 52, 3349.
  • Borsuk, M. E., C. A. Stow, and K. H. Reckhow (2003), Integrated approach to total maximum daily load development for neuse river estuary using Bayesian probability network model (Neu-BERN), J. Water Resour. Plann. Manage., 129, 271.
  • Borsuk, M. E., C. A. Stow, and K. H. Reckhow (2004), Confounding effect of flow on estuarine response to nitrogen loading, J. Environ. Eng., 130, 605614.
  • Butcher, J. B., E. Thirolle, E. Booth, and A. Rooker (2003), Estimating Limited-Data Urban Bacterial TMDLs Using Empirical Bayes Regionalization, ASCE, Philadelphia, Pennsylvania.
  • Canham, C. D. W., J. Cole, and W. K. Lauenroth (Eds.) (2003), Models in ecosystem science, Princeton University Press, Princeton.
  • Carlin, B. P. and T. A. Louis (2000), Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed., Chapman & Hall/CRC, Boca Raton.
  • Caselton, W. F., L. Kan, and J. Zidek (1992), Quality data networks that minimize entropy, in Statistics in the Environmental & Earth Sciences, edited by A. T. Walden and P. Guttorp, pp. 1038, E. Arnold, London.
  • Christakos, G. (2000), Modern Spatiotemporal Geostatistics, Oxford University Press, New York.
  • Christakos, G. and X. Li (1998), Bayesian maximum entropy analysis and mapping: A farewell to kriging estimators?, Math. Geol., 30, 435462.
  • Deamer, N. (2009), Neuse river basinwide water quality planRep., NC Department of Environment and Natural Resources: Division of Water Quality, Planning Section – Basinwide Planning Unit, Raleigh, NC.
  • Environmental Protection Agency (1994), Statistical Training Course on Ground Water Monitoring Data Analysis Report, pp. 196, Washington, DC.
  • Environmental Protection Agency (2001), The National Costs of the Total Maximum Daily Load Program (Draft Report), pp. 51, United States Environmental Protection Agency, Washington, D.C.
  • French, S., J. Maule, N. Papamichail, and E. Corporation (2009), Decision Behaviour, Analysis and Support, Cambridge University Press, New York.
  • Gelman, A. (2002), Prior distribution, in Encyclopedia of Environmetrics, edited by A. H. El-Shaarawi and W. W. Piegorsch, pp. 16341637, Wiley, Chichester.
  • Gelman, A. (2006), Prior distributions for variance parameters in hierarchical models, Bayesian Anal., 1, 515533.
  • Guttorp, P. and P. D. Sampson (1994), Methods for estimating heterogeneous spatial covariance functions with environmental applications, Handbook Stat., 12, 661689.
  • Guttorp, P., P. D. Sampson, and K. Newman (1992), Nonparametric estimation of spatial covariance with application to monitoring network evaluation, in Statistics in the Environmental & Earth Sciences, edited by A. T. Walden and P. Guttorp, pp. 3951, E. Arnold, London.
  • Harker, P. T. and L. G. Vargas (1987), The Theory of Ratio Scale Estimation: Saaty's Analytic Hierarchy Process, Manag. Sci., 33, 13831403.
  • Harmancioglu, N. B. and N. Alpaslan (1992), Water quality monitoring network design: a problem of multi-objective decision making, J. Am. Water Resour. Assoc., 28, 179192.
  • Huang, Y. S., J. T. Liao, and Z. L. Lin (2009), A study on aggregation of group decisions, Syst. Res. Behav. Sci., 26, 445454.
  • Jaynes, E. (1963), Information theory and statistical mechanics, in Statistical Physics, edited by K. W. Ford, pp. 181218, Benjamin, New York.
  • Karamouz, M., A. Nokhandan, R. Kerachian, and Č. Maksimovic (2009), Design of on-line river water quality monitoring systems using the entropy theory: A case study, Environ. Monit. Assess., 155, 6381.
  • Le, N. D. and J. V. Zidek (1994), Recent advances in statistics and probability. Proceedings of the 4th International Meeting of Statistics in the Basque Country, edited by J. P. Vilaplana and M. L. Puri, pp. 191206, VSP, Utrecht, The Netherlands.
  • Le, N. D. and J. V. Zidek (2006), Statistical Analysis of Environmental Space-Time Processes, Springer, New York.
  • Le, N. D., W. Sun, and J. V. Zidek (1997), Bayesian multivariate spatial interpolation with data missing by design, J. R. Stat. Soc: Ser. B, 59, 501510.
  • Li, C., V. P. Singh, and A. K. Mishra (2012), Entropy theory-based criterion for hydrometric network evaluation and design: Maximum information minimum redundancy, Water Resour. Res., 48, W05521.
  • LoBuglio, J. N., G. W. Characklis, and M. L. Serre (2007), Cost-effective water quality assessment through the integration of monitoring data and modeling results, Water Resour. Res., 43, W03435.
  • Lootsma, F. A. (1988), Numerical scaling of human judgement in pairwise-comparison methods for fuzzy multi-criteria decision analysis, in Mathematical Models for Decision Support, edited by G. Mitra, pp. 5788, Springer-Verlag, Berlin.
  • Lootsma, F. A. (1993), Scale sensitivity in the multiplicative AHP and SMART, J. Multicriteria Decis. Anal., 2, 87110.
  • Luettich, R. A., J. E. McNinch, H. W. Paerl, C. H. Peterson, J. T. Wells, M. Alperin, C. S. Martens, and J. L. Pinckney (2000a), Neuse River Estuary modeling and monitoring project stage 1: hydrography and circulation, water column nutrients and productivity, sedimentary processes and benthic-pelagic coupling Report, pp. 172, Water Resources Research Institute of the University of North Carolina, Raleigh, NC.
  • Luettich, R. A., J. E. McNinch, H. W. Paerl, C. H. Peterson, J. T. Wells, M. Alperin, C. S. Martens, and J. L. Pinckney (2000b), Neuse River Modeling and Monitoring Study (ModMon); Phase 1, 1997–1999 Report, pp. 172, Water Resources Research Institute, Raleigh, NC.
  • Markus, M., H. Vernon Knapp, and G. D. Tasker (2003), Entropy and generalized least square methods in assessment of the regional value of streamgages, J. Hydrol., 283, 107121.
  • Marshall, K. T. and R. M. Oliver (1995), Decision Making and Forecasting, McGraw-Hill, New York.
  • Mishra, A. K. and P. Coulibaly (2009), Developments in hydrometric network design: A review, Rev. Geophys., 47, RG2001.
  • Murphy, B. B. and R. D. Morrison (Eds.) (2002), Introduction to Environmental Forensics, Academic Press, San Diego.
  • National Research Council (2001), Assessing the TMDL Approach to Water Quality Management Report, Washington, D.C.
  • NC Department of Environment and Natural Resources (1999), Total Maximum Daily Load for Total Nitrogen to the Neuse River Estuary, North Carolina Report, Raleigh, NC.
  • Ott, W. R. (1995), Environmental Statistics and Data Analysis, CRC Press L.L.C, Boca Raton.
  • Ozkul, S., N. B. Harmancioglu, and V. P. Singh (2000), Entropy-based assessment of water quality monitoring networks, J. Hydrologic Eng., 5, 90100.
  • Paerl, H. W. (1987), Dynamics of Blue-Green Algal (Microcystis Aeruginosa) Blooms in the Lower Neuse River, North Carolina: Causative Factors and Potential Controls Report, Raleigh, N.C.
  • Paerl, H. W. (2006), Assessing and managing nutrient-enhanced eutrophication in estuarine and coastal waters: Interactive effects of human and climatic perturbations, Ecol. Eng., 26, 4054.
  • Paerl, H. W., M. A. Mallin, C. A. Donahue, M. Go, and B. L. Peierls (1995), Nitrogen loading sources and eutrophication of the Neuse River estuary, North Carolina: Direct and indirect roles of atmospheric deposition Report PB−96–115217/XAB, pp. 130, North Carolina Water Resources Research Inst., Raleigh, NC.
  • Paerl, H. W., L. M. Valdes, M. F. Piehler, and C. A. Stow (2006), Assessing the effects of nutrient management in an estuary experiencing climatic change: The Neuse River Estuary, North Carolina, Environ. Manage., 37, 422436.
  • Paerl, H. W., L. M. Valdes-Weaver, A. R. Joyner, and V. Winkelmann (2007), Phytoplankton indicators of ecological change in the eutrophying Pamlico Sound system, North Carolina, Ecol. Appl., 17, 88-101.
  • Paerl, H. W., K. Rossignol, S. Hall, B. Peierls, and M. Wetz (2009), Phytoplankton community indicators of short- and long-term ecological change in the anthropogenically and climatically impacted Neuse River Estuary, North Carolina, USA, Estuaries Coasts, 33, 485497.
  • Paerl, H. W., K. Rossignol, S. Hall, B. Peierls, and M. Wetz (2010), Phytoplankton community indicators of short- and long-term ecological change in the anthropogenically and climatically impacted Neuse River Estuary, North Carolina, USA, Estuaries Coasts, 33, 485497.
  • Pinckney, J. L., H. W. Paerl, M. B. Harrington, and K. E. Howe (1998), Annual cycles of phytoplankton community structure and bloom dynamics in the Neuse River Estuary, NC, Marine Biol., 131, 371381.
  • Pollice, A. and G. Jona Lasinio (2010), A multivariate approach to the analysis of air quality in a high environmental risk area, Environmetrics, 21, 741754.
  • Puangthongthub, S., S. Wangwongwatana, R. M. Kamens, and M. L. Serre (2007), Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network, Atmos. Environ., 41, 77887805.
  • Qian, S. S. (1997), Estimating the area affected by phosphorus runoff in an Everglades wetland: A comparison of universal kriging and Bayesian kriging, Environ. Ecol. Stat., 4, 129.
  • Qian, S. S. and K. H. Reckhow (2007), Combining model results and monitoring data for water quality assessment, Environ. Sci. Technol., 41, 50085013.
  • Qian, S. S., M. E. Borsuk, and C. A. Stow (2000), Seasonal and long-term nutrient trend decomposition along a spatial gradient in the Neuse River Watershed, Environ. Sci. Technol., 34, 44744482.
  • Reckhow, K. H. (1996), Improved estimation of ecological effects using an empirical Bayes method, J. Am. Water Resour. Assoc., 32, 929935.
  • Saaty, R. W. (1987), The analytic hierarchy process-what it is and how it is used, Math. Model., 9, 161176.
  • Saaty, T. L. (1977), A scaling method for priorities in hierarchical structures, J. Math. Psychol., 15, 234281.
  • Saaty, T. L. (1986), Axiomatic foundation of the analytic hierarchy process, Manag. Sci., 32, 841855.
  • Saaty, T. L. (1990), The Analytic Hierarchy Process: Planning, priority setting, resource allocation, 2nd ed., McGraw-Hill, Pittsburgh, PA.
  • Saaty, T. L. (1994), Fundamentals of Decision Making, RWS Publications, Pittsburgh.
  • Saaty, T. L. (2005), Analytic Hierarchy Process, in Encyclopedia of Biostatistics, Wiley, Chichester, West Sussex.
  • Sampson, P. D. and P. Guttorp (1992), Nonparametric estimation of nonstationary spatial covariance structure, J. Am. Stat. Assoc., 87, 108119.
  • Sanders, T. G., R. C. Ward, J. C. Loftis, T. D. Steele, D. D. Adrian, and V. Yevjevich (1987), Design of Networks For Monitoring Water Quality, 2nd ed., Water Resources Publications, LLC.
  • Simeonov, V., J. A. Stratis, C. Samara, G. Zachariadis, D. Voutsa, A. Anthemidis, M. Sofoniou, and T. Kouimtzis (2003), Assessment of the surface water quality in Northern Greece, Water Res., 37, 41194124.
  • Solow, A. R., and A. Gaines, G. (1995), An empirical bayes approach to monitoring water quality, Environmetrics, 6, 15.
  • Stein, W. E. and P. J. Mizzi (2007), The harmonic consistency index for the analytic hierarchy process, Eur. J. Oper. Res., 177, 488497.
  • Stow, C. A., C. Roessler, M. E. Borsuk, J. D. Bowen, and K. H. Reckhow (2003), Comparison of estuarine water quality models for total maximum daily load development in Neuse River Estuary, J. Water Resour. Plann. Manag., 129, 307314.
  • Strobl, R. O. and P. D. Robillard (2008), Network design for water quality monitoring of surface freshwaters: A review, J. Environ. Manag., 87, 639648.
  • Vaidya, O. S. and S. Kumar (2006), Analytic hierarchy process: An overview of applications, Eur. J. Oper. Res., 169, 129.
  • Valdes-Weaver, L. M., M. F. Piehler, J. L. Pinckney, K. E. Howe, K. Rossignol, and H. W. Paerl (2006), Long-term temporal and spatial trends in phytoplankton biomass and class-level taxonomic composition in the hydrologically variable Neuse-Pamlico Estuarine Continuum, North Carolina, U.S.A, Limnol. Oceanogr., 51, 14101420.
  • Van Den Honert, R. C. and F. A. Lootsma (1997), Group preference aggregation in the multiplicative AHP The model of the group decision process and Pareto optimality, Eur. J. Oper. Res., 96, 363370.
  • Van Dongen, S. (2006), Prior specification in Bayesian statistics: Three cautionary tales, J. Theor. Biol., 242, 90100.
  • Wahba, G. and J. Wendelberger (1980), Some new mathematical methods for variational objective analysis using splines and cross-validation, Month. Weather Rev., 108, 11221143.
  • Wilde, F. D. (2005), Preparations for water sampling, in National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, pp. 46, U.S. Geological Survey.
  • Yoon, K. P. and C. L. Hwang (1995), Multiple attribute decision making: an introduction, Sage Publications, Thousand Oaks, CA.