期刊
HYDROLOGICAL SCIENCES JOURNAL
卷 57, 期 3, 页码 383-406出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2012.665994
关键词
missing precipitation data; spatial interpolation; genetic algorithms; nonlinear optimization methods; mixed integer mathematical programming (MINLP); multiple linear regression; nonlinear least squares approach; thin-plate splines; trend surface models; Kentucky; USA
New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some nave approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.
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