Modelling hydrodynamics in the Rio Parana, Argentina : an evaluation and inter-comparison of reduced-complexity and physics based models applied to a large sand-bed river

Depth-averaged velocities and unit discharges within a 30 km reach of one of the world's largest rivers, the Rio Parana, Argentina, were simulated using three hydrodynamic models with different process representations: a reduced complexity (RC) model that neglects most of the physics governing fluid flow, a two-dimensional model based on the shallow water equations, and a three-dimensional model based on the Reynolds-averaged Navier-Stokes equations. Row characteristics simulated using all three models were compared with data obtained by acoustic Doppler current profiler surveys at four cross sections within the study reach. This analysis demonstrates that, surprisingly, the performance of the RC model is generally equal to, and in some instances better than, that of the physics based models in terms of the statistical agreement between simulated and measured flow properties. In addition, in contrast to previous applications of RC models, the present study demonstrates that the RC model can successfully predict measured flow velocities. The strong performance of the RC model reflects, in part, the simplicity of the depth-averaged mean flow patterns within the study reach and the dominant role of channel-scale topographic features in controlling the flow dynamics. Moreover, the very low water surface slopes that typify large sand-bed rivers enable flow depths to be estimated reliably in the RC model using a simple fixed-lid planar water surface approximation. This approach overcomes a major problem encountered in the application of RC models in environments characterised by shallow flows and steep bed gradients. The RC model is four orders of magnitude faster than the physics based models when performing steady-state hydrodynamic calculations. However, the iterative nature of the RC model calculations implies a reduction in computational efficiency relative to some other RC models. A further implication of this is that, if used to simulate channel morphodynamics, the present RC model may offer only a marginal advantage in terms of computational efficiency over approaches based on the shallow water equations. These observations illustrate the trade off between model realism and efficiency that is a key consideration in RC modelling. Moreover, this outcome highlights a need to rethink the use of RC morphodynamic models in fluvial geomorphology and to move away from existing grid-based approaches, such as the popular cellular automata (CA) models, that remain essentially reductionist in nature. In the case of the world's largest sand-bed rivers, this might be achieved by implementing the RC model outlined here as one element within a hierarchical modelling framework that would enable computationally efficient simulation of the morphodynamics of large rivers over millennial time scales. (C) 2012 Elsevier B.V. All rights reserved.

Comparing simulation methods for modelling the errors of stand inventory data

Abstract

Modelling of vegetative filter strips in catchment scale erosin control

Selostus: Valuma-aluetason mallisovellus suojakaistojen käytöstä eroosion torjunnassa

Micro-Computer Based Linear Scheduling Application for Highway Construction Project Control, HR-339A, 1995

This report describes a new approach to the problem of scheduling highway construction type projects. The technique can accurately model linear activities and identify the controlling activity path on a linear schedule. Current scheduling practices are unable to accomplish these two tasks with any accuracy for linear activities, leaving planners and manager suspicious of the information they provide. Basic linear scheduling is not a new technique, and many attempts have been made to apply it to various types of work in the past. However, the technique has never been widely used because of the lack of an analytical approach to activity relationships and development of an analytical approach to determining controlling activities. The Linear Scheduling Model (LSM) developed in this report, completes the linear scheduling technique by adding to linear scheduling all of the analytical capabilities, including computer applications, present in CPM scheduling today. The LSM has tremendous potential, and will likely have a significant impact on the way linear construction is scheduled in the future.

A general linear relaxometry model of R1 using imaging data.

PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Nanoparticles in SiH4-Ar plasma: Modelling and comparison with experimental data

Experimental and theoretical investigations for growth of silicon nanoparticles (4 to 14 nm) in radio frequency discharge were carried out. Growth processes were performed with gas mixtures of SiH4 and Ar in a plasma chemical reactor at low pressure. A distinctive feature of presented kinetic model of generation and growth of nanoparticles (compared to our earlier model) is its ability to investigate small"critical" dimensions of clusters, determining the rate of particle production and taking into account the influence of SiH2 and Si2Hm dimer radicals. The experiments in the present study were extended to high pressure (≥20 Pa) and discharge power (≥40 W). Model calculations were compared to experimental measurements, investigating the dimension of silicon nanoparticles as a function of time, discharge power, gas mixture, total pressure, and gas flow.