Abstract
A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.
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References
R.A. Freeze, J. Massmann, L. Smith, T. Sperling and B. James, Ground Water 28 (1990) 738.
D.R. Davis and W.M. Dvoranchik, Water Resour. Res. 7 (1971) 700.
G.J. Vicens, I. Rodriguez-Iturbe and J.C. Schaake Jr., Water Resour. Res. 11 (1975) 405.
H.H. Einstein and G.B. Baecher, Rock Mechanics and Rock Engineering 16 (1983) 39.
J.S. Gates and C.C. Kisiel, Water Resour. Res. 10 (1974) 1031.
E.G. Reichard and J.S. Evans, Water Resour. Res. 25 (1989) 1451.
P.W. Grosser and A.S. Goodman, Civ. Eng. Syst. 2 (1985) 186.
M. Ben-Zvi, B. Berkowitz and S. Kesler, Water Resour. Manage. 2 (1988) 11.
C.M. Marin, M.A. Medina, Jr. and B. Butcher, J. Contam. Hydrol. 5 (1989) 1.
M.A. Medina, Jr., B. Butcher and M.M. Carlos, J. Contam. Hydrol. 5 (1989) 15.
B.R. James and R.A. Freeze, Water Resour. Res. 29 (1993) 2049.
B.R. James and S.M. Gorelick, Water Resour. Res. 30 (1994) 3499.
K.C. Abbaspour, Agricultural and Forest Meteorology 71 (1994) 297.
H. Raiffa and R. Schlaifer,Applied Statistical Decision Theory, The M.I.T. Press, Cambridge, MA, 1961.
K.C. Abbaspour, R. Schulin and E. Schläppi, submitted for review to Mathematical Geology (1996).
K.C. Abbaspour, M.Th. van Genuchten, R. Schulin and E. Schläppi, submitted for review to Water Resources Research (1996).
C. Martinson, Eclogae Geologicae Helvetiae 87 (1994) 473.
J.R. Benjamin and C.A. Cornell,Probability, Statistics, and Decision for Civil Engineers, McGraw-Hill, New York, 1970.
M.H. DeGroot,Probability and Statistics, Addison-Wesley, Reading, MA, 1975.
M.D. McKay, R.J. Beckman and W.J. Conover, Technometrics 21 (1979) 239.
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Abbaspour, K.C., Schulin, R., Schläppi, E. et al. A Bayesian approach for incorporating uncertainty and data worth in environmental projects. Environ Model Assess 1, 151–158 (1996). https://doi.org/10.1007/BF01874902
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DOI: https://doi.org/10.1007/BF01874902