methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic vari...
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methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system's interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.
In this paper, an open performance model framework PMPS(n) and a realization of this framework PMPS(3), including memory, I/O and network, are presented and used to predict runtime of NPB benchmarks on P4 cluster. The...
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ISBN:
(纸本)3540400567
In this paper, an open performance model framework PMPS(n) and a realization of this framework PMPS(3), including memory, I/O and network, are presented and used to predict runtime of NPB benchmarks on P4 cluster. The experimental results demonstrates that PMPS(3) can work much better than PERC for I/O intensive applications, and can do as well as PERC for memory-intensive applications. Through further analysis, it is indicated that the results of the performance model can be influenced by the data correlations, control correlations and operation overlaps and which must be considered in the models to improve the prediction precision. The experimental results also showed that PMPS(n) be of great scalability.
Let Y-n,Y-k, k = 0, 1, 2, ..., n greater than or equal to 1, be a collection of random variables, where for each n, Y-n,Y-k, k = 0, 1, 2,..., are independent. Let A = [p(n, k)] be a regular summability method. We prov...
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Let Y-n,Y-k, k = 0, 1, 2, ..., n greater than or equal to 1, be a collection of random variables, where for each n, Y-n,Y-k, k = 0, 1, 2,..., are independent. Let A = [p(n, k)] be a regular summability method. We provide some rates of convergence (Berry-Esseen type bounds) for the weak convergence of summability transform (AY). We show that when A = [p(n,k)] is the classical Cesaro summability method, the rate of convergence of the resulting central limit theorem is best possible among all regular triangular summability methods with rows adding up to one. We further provide some summability results concerning l(2)-negligibility. An application of these results characterizes the rate of convergence of Schnabl operators while approximating Lipschitz continuous functions.
By using the concept of statistical convergence we present statistical Tauberian theorems of gap type for the Cesaro, Euler-Borel family and the Hausdorff families applicable in arbitrary metric spaces. In contrast to...
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By using the concept of statistical convergence we present statistical Tauberian theorems of gap type for the Cesaro, Euler-Borel family and the Hausdorff families applicable in arbitrary metric spaces. In contrast to the classical gap Tauberian theorems, we show that such theorems exist in the statistical sense for the convolution methods which include the Taylor and the Borel matrix methods. We further provide statistical analogs of the gap Tauberian theorems for the Hausdorff methods and provide an explanation as to how the Tauberian rates over the gaps may differ from those of the classical Tauberian theorems. (C) 2003 Elsevier Inc. All rights reserved.
The concept of statistical convergence, which is related to the usual concept of convergence in probability, provides a regular summability method for abstract metric spaces. By using probabilistic tools, we provide s...
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The concept of statistical convergence, which is related to the usual concept of convergence in probability, provides a regular summability method for abstract metric spaces. By using probabilistic tools, we provide some Tauberian theorems which have best possible order Tauberian conditions. Furthermore, these methods can be used to unify and improve the classical pointwise Tauberian theorems of summability theory for the random walk type methods as proved by Bingham, and Hausdorff methods as proved by Lorentz. (C) 1998 Academic Press.
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