Groundwater long-term monitoring (LTM) is a costly activity required at most subsurface remediation sites. Many existing LTM networks need to be optimized to reflect changes in site conditions and to increase their effectiveness in defining the plume. A spatial analysis method using Delaunay triangulation techniques was developed to eliminate redundant monitoring points and to locate new wells where additional data are needed. This method uses Delaunay triangulation of the monitoring network for site discretization and assesses the concentration estimation error at each monitoring location to judge its relative contribution to the spatial plume characterization. Locations where the concentration estimation error is small are considered redundant and become candidates for elimination. New monitoring locations are identified where the projected concentration estimation errors are high. Tests comparing the Delaunay method to a fate and transport analytical model illustrated the attributes and effectiveness of the method. Application to a benzene plume site demonstrated that results from Delaunay triangulation agree well with geostatistical approaches. Although the method is relatively less accurate, and lacks the resolution obtained with the geostatistical approach, it is computationally efficient and simple to implement by non-statisticians.
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Following some general remarks on the importance and unimportance of optimization in spatial network design, we take up, in modest detail, how one might exploit spatial autocorrelations and covariateinformation. We point out that spatial autocorrelations themselves require care in their estimation andthen proceed with two illustrations to show how probabilistic error calculations are made for mappingproblems using network station data. One illustration uses a quantitative mapping variable, and the otheruses a qualitative mapping variable.
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Cokriging is used to merge rain gage measurements and radar rainfall data. The cokriging estimators included are ordinary, universal, and disjunctive. To evaluate the estimators, two simulation experiments are performed. The first experiment assumes that high-quality radar rainfall fields are ground truth rainfall fields. From each ground truth rainfall field, multiple combinations of rain gage measurement field and radar rainfall field are artificially generated with varying gage network density and error characteristics of radar rainfall. The second experiment uses a stochastic space-time rainfall model to generate assumed ground truth rainfall fields of various characteristics. Due to the sparsity of rain gage measurements, the second-order statistics required for cokriging can only be estimated with large uncertainty. The adverse effects of this uncertainty, and the point sampling error of rain gage measurements are explicitly assessed by cokriging the ground truth rainfall data and the radar rainfall data with near perfectly known second-order statistics.
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A large number of hydrological phenomena may be regarded as realizations of space-time random functions. Most available hydrological data sets exhibit time-rich/space-poor characteristics, as well as, some form of temporal periodicity and spatial non-stationarity. To better understand the space-time structure of such hydrological variables, the observed values at each measurement site are considered as separate, but correlated time series. Moreover, it is assumed that the time series are realizations of a mixture of random functions, each associated with a different temporal scale, represented by a particular basic variogram. To preserve the observed temporal periodicities, the experimental direct and cross variograms are modelled as linear combinations of a number of hole function variograms. In a further step, the principal component analysis is used to determine groupings of measurement stations at different temporal scales. The proposed procedure is then applied to monthly piezometric data in a basin south of Paris, France. The temporal scales are determined to be the 12-month seasonal and the 12-year climatic cycles. At each temporal scale different spatial groupings are observed which are attributed to the contrast between the nearly steady state climatic variations versus the almost transient seasonal fluctuations.
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An entropy approach to data collection network design
Author: Donald H. Burn,Yujuin Yang | Size: 1.3 MB | Format:PDF | Quality:Unspecified | Publisher: Elsevier | Year: 1994 | pages: 307-324 | ISBN: --
A new methodology is developed for data collection network design. The approach employs a measure of the information flow between gauging stations in the network which is referred to as the directional information transfer. The information flow measure is based on the entropy of gauging stations and pairs of gauging stations. Non-parametric estimation is used to approximate the multivariate probability density functions required in the entropy calculations. The potential application of the approach is illustrated using extreme flow data from a collection of gauging stations located in southern Manitoba, Canada.
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The new Autodesk Inc. Design and Creation Suites offer unprecedented access to the Autodesk 2014 software portfolio spanning the desktop and cloud and allow building, product, plant, and factory design; engineering, construction and infrastructure; and entertainment creation professionals to innovate more. Autodesk Revit AMS 2014 is the 3-in-1 version, that includes features for architectural design, MEP and structural engineering.
Autodesk Revit software delivers tools that support architectural design, MEP engineering, structural engineering, and construction. Revit is specifically built for Building Information Modeling (BIM) to help you design, build, and maintain higher-quality, more energy-efficient buildings. Comprehensive features make it an ideal solution for the entire building project team.
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The information on .ISO
- Initial material for creation of assembly - original (32bit & 64bit) ESD-distribution kits.
- In the distribution kit libraries (only English are added!) for their installation without connection to the Internet.
- The technical improvements relieving the end user from of some problems at installation of the program are lead.
- The distribution kit is converted in the optimized ISO-image.
- The file of autostart is excluded from the distribution kit. For the beginning installations Autodesk Revit Architecture 2014 it is necessary to start file SetupRA.exe, for installation Autodesk Revit MEP 2014 - SetupRM.exe, for Autodesk Revit Structure 2014 - SetupRS.exe
Folders with 21 RAR files + 4 recovery files (you can download only the RAR files)
Recovery volumes or .rev files are special files which can be created by WinRAR/RAR and allow you to reconstruct missing and damaged files in a volume set. They can only be used with multi-volume archives.
Each recovery volume is able to reconstruct one missing RAR volume. For example, if you have 30 volumes and 3 recovery volumes, you are able to reconstruct any 3 missing volumes. If the number of .rev files is less than a number of missing volumes, reconstructing is impossible.
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Posted by: pezhmankhan - 04-08-2013, 07:26 AM - Forum: Steel
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ASM Handbook, Volume 6: Welding, Brazing, and Soldering
Author: Kelly Ferjutz, Joseph R. Davis | Size: 49.8 MB | Format:PDF | Quality:Unspecified | Publisher: ASM | Year: 1994 | pages: 2873 | ISBN: 0871703823
Volume 6 is the most comprehensive reference book ever produced on the major joining technologies and their applications to engineered materials--90% of its contents are totally new to the ASM Handbook Series. With over 500 illustrations and 400 tables, this book includes practical advice on consumable selection and procedure development, as well as joining fundamentals.
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This paper, the first in a series of two, develops an entropy-based approach for evaluating
rainfall networks. Space and time dependencies between raingages are examined by autocovariance
and cross-covariance matrices. Multivariate distributions, associated with different dependencies, are
obtained using the principle of maximum entropy (POME). Formulas for entropy (uncertainty in data
of one raingage), joint entropy (uncertainty in data of two or more raingages) and transinformation
(common information content among two or more raingages) are derived for each distribution, based
on normal data. The decision whether to keep or eliminate a raingage depends entirely on reduction
or gain of information at that raingage. The lines of equal information (isoinformation contours are
defined by considering two raingages (bivariate case) and many raingages (multivariate case).
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