Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing *** existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is ...
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Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing *** existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope *** objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length,and to evaluate its predictive capability at different resolution *** grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be *** the multiple flow direction algorithm united with the transfer grid(MFDTG),the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope *** effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern *** simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5%showed that the relationship between surface-runoff intensity and slope length was a negative power *** by the proposed modified L-equation((al_(x)^((b+1))/22.13)^(m)),the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1,5,25 and 90 m resolutions *** study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst *** attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.
A real world supply chain planning starts with the demand forecasting as a key input. In most scenarios, especially in fields like e-commerce where demand patterns are complex and are large scale, demand forecasting i...
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ISBN:
(纸本)9781450392365
A real world supply chain planning starts with the demand forecasting as a key input. In most scenarios, especially in fields like e-commerce where demand patterns are complex and are large scale, demand forecasting is done independent of supply chain constraints. There have been a plethora of methods, old and recent, for generating accurate forecasts. However, to the best of our knowledge, none of the methods take supply chain constraints into account during forecasting. In this paper, we are primarily interested in supply chain aware forecasting methods that does not impose any restrictions on demand forecasting process. We assume that the base forecasts follow a distribution from exponential family and are provided as input to supply chain planning by specifying the distribution form and parameters. With this in mind, following are the contributions of our paper. First, we formulate the supply chain aware forecast improvement of a base forecast as finding the game theoretically optimal parameters satisfying the supply chain constraints. Second, for regular distributions from exponential family, we show that this translates to projecting base forecast onto the (convex) set defined by supply constraints, which is at least as accurate as the base forecasts. Third, we note that using off the shelf convex solvers does not scale for large instances of supply chain, which is typical in e-commerce settings. We propose algorithms that scale better with problem size. We propose a general gradient descent based approach that works across different distributions from exponential family. We also propose a network flow based exact algorithm for Laplace distribution (which relates to mean absolute error, which is the most commonly used metric in forecasting). Finally, we substantiate the theoretical results with extensive experiments on a real life e-commerce data set as well as a range of synthetic data sets.
A dual algorithm for the submodular flow problem is proposed. The concept of the 'best improving set' is used to increase the dual objective as fast as possible, which is the kind of steepest ascent method emp...
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A dual algorithm for the submodular flow problem is proposed. The concept of the 'best improving set' is used to increase the dual objective as fast as possible, which is the kind of steepest ascent method employed by R. Hassin for the minimum cost now problem. For the dual optimal solution thus obtained, the associated submodular flow is constructed by complementary slackness.
We show that the O( K · n 4 ) algorithm of Hamacher (1982) for finding the K best cut-sets fails because it may produce cuts rather than cut-sets. With the convention that two cuts (X, X ) and (Y, Y ) are differe...
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We show that the O( K · n 4 ) algorithm of Hamacher (1982) for finding the K best cut-sets fails because it may produce cuts rather than cut-sets. With the convention that two cuts (X, X ) and (Y, Y ) are different whenever X ≠ Y the K best cut problem can be solved in O( K · n 4 ).
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