Linear Programming Method Considering Topographical Factors Used for Estimating Missing Precipitation
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 5
Abstract
A new linear programming method with an option for topographical factors is developed for estimating missing precipitation. It is simply assumed that missing precipitation depth at a base station is expressed as a linear combination of precipitation depths at neighboring index stations in the same period using weighting factors. Also, the topographical factor, which is proportional to the weighting factor, is introduced into the method. The topographical factor is associated with distance and difference in elevation between the base station and the index station. In this research two case studies show an introduction of the topographical factors into the existing linear programming method for estimating missing precipitation and makes weighting factors in the method change into those reflecting the topography of precipitation points. The developed method with an option is useful in estimating the missing precipitation values in the case of hilly regions only when the option is taken after applying four options.
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References
Abebe, A. J., Solomatine, D. P., and Venneker, R. G. W. (2000). “Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events.” Hydrol. Sci. J., 45(3), 425–436.
Kalteh, A. M., and Hjorth, P. (2009). “Imputation of missing values in a precipitation-runoff process database.” Hydrol. Res., 40(4), 420–432.
Kim, J. O., and Curry, J. (1977). “The treatment of missing data in multivariate analysis.” Sociol. Meth. Res., 6(2), 215–240.
KOWACO. (2005). “Annual report of hydrologic data.” Research and Planning, Korea Water Resources Corporation (KOWACO), Republic of Korea.
Kruizinga, S., and Yperlaan, G. J. (1978). “Spatial interpolation of daily totals of rainfall.” J. Hydrol., 36(1–2), 65–73.
Kuligowski, R. J., and Barros, A. P. (1998). “Using artificial neural networks to estimate missing rainfall data.” J. Am. Water Res. Assn., 34(6), 1437–1447.
Linear Interactive and Discrete Optimizer (LINDO) Systems, Inc., 〈http://www.lindo.com/〉.
Linsley, R. K., Jr., Kohler, M. A., and Paulhus, J. H. (1988). Hydrology for engineers, McGraw-Hill, New York.
Little, R. J. A., and Runbin, D. B. (1987). Statistical analysis with missing data, Wiley, New York.
Malhotra, N. K. (1987). “Analyzing marketing research data with incomplete information on the dependent variable.” J. Market Res., 24(1), 74–84.
Ministry of Land, Transport, and Maritime Affairs (MLTM). (2008). “Report on master planning of flood control in 2nd region of Han River Basin (a draft submitted for deliberation).” Ministry of Land, Transport, and Maritime Affairs, Republic of Korea.
Paulhus, J. L. H., and Kohler, M. A. (1952). “Interpolation of missing precipitation records.” Mon. Weather Rev., 80(8), 129–133.
Shearman, R. J., and Salter, P. M. (1975). “An objective rainfall interpolation and mapping technique.” Hydrol. Sci. Bull., 20(3), 353–363.
Simanton, J. R., and Osborn, H. B. (1980). “Reciprocal distance estimate of point rainfall.” J. Hydraul. Eng., 106(7), 1242–1246.
Singh, V. P. (1989). Hydrologic systems, watershed modeling, Vol. II, Prentice-Hall, Upper Saddle River, NJ.
Tabios, G. Q., III, and Salas, J. D. (1985). “A comparative analysis of techniques for spatial interpolation of precipitation.” Water Resour. Bull., 21(3), 365–380.
Teegavarapu, R. S. V., and Chandramouli, V. (2005). “Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records.” J. Hydrol., 312(1–4), 191–206.
Tsikriktsis, N. (2005). “A review of techniques for treating missing data in OM survey research.” J. Oper. Manage., 24(1), 53–62.
Tung, Y. K. (1983). “Point rainfall estimation for a mountainous region.” J. Hydraul. Eng., 109(10), 1383–1393.
Wei, T. C., and McGuinness, J. L. (1973). “Reciprocal distance squared method, a computer technique for estimating area precipitation.”, U.S. Agricultural Research Service, North Central Region, OH.
Yoo, J. H. (2010). “A certification of linear programming method for estimating missing precipitation values ungauged.” J. Korean Water Resour. Assoc., 43(3), 257–264.
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Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 5 • May 2013
Pages: 542 - 551
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Sep 4, 2010
Accepted: Feb 3, 2012
Published online: Feb 6, 2012
Published in print: May 1, 2013
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