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Ramesh S.V. Teegavarapu 《水文科学杂志》2013,58(3):383-406
Abstract 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 naïve 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. Editor D. Koutsoyiannis; Associate editor S. Grimaldi Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383–406. 相似文献
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Fahad Khan Khadim 《水文科学杂志》2020,65(3):427-441
ABSTRACTA Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved in this study to optimize environmental sustainability of flood control, drainage, and irrigation (FCDI) projects in the deltaic regions of Bangladesh. The model optimizes the value of integrated resource benefit, a dimensionless variable defined to measure the environmental sustainability based on the water, agricultural and ecological resources, with a set of project interventions being the major drivers. The resource benefits were evaluated with the help of several indicators, such as flood, navigability, salinity, waterlogging, cropping intensity, land loss and vegetation. The solution of MINLP model provided optimal values of the decision variables, which are the quantities of project interventions (e.g. length and height of dike, number of sluices and drainage inlets, lengths of drainage canals, erosion protection and afforestation works). The approach and the MINLP formulation presented in this study can be used for any real-life FCDI project improvements. 相似文献
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