Hydraulic Parameter Identification
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Hydraulic Parameter Identification
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Author : Luc C. Lebbe
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Hydraulic Parameter Identification written by Luc C. Lebbe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
Hydraulic parameter identification is a crucial step in hydrogeological investigations. The book proposes a unique and generalized interpretation method for single and multiple pumping tests made in groundwater reservoirs with layered heterogeneity and with or without lateral anisotropy. This method eliminates the drawbacks of the numerous and frequently applied interpretation methods. The book also presents an introduction to inverse modeling, resulting in optimal parameter values with their joint confidence region and the corresponding residuals. Cross sections through this multidimensional region elucidate the relation between the shape of this region and some statistical parameters describing the reliability of the identified parameters. This method is demonstrated by means of five pumping or recharge tests.
Parameter Identification And Inverse Problems In Hydrology Geology And Ecology
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Author : Johannes Gottlieb
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Parameter Identification And Inverse Problems In Hydrology Geology And Ecology written by Johannes Gottlieb and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
The Workshop on Parameter Identification and Inverse Problems in Hydrology, Geology and Ecology, Karlsruhe, April 10-12, 1995, was organized to bring to gether an interdisciplinary group drawn from the areas of science, engineering and mathematics for the following purposes: - to promote, encourage and influence more understanding and cooperation in the community of parameter identifiers from various disciplines, - to forge unity in diversity by bringing together a variety of disciplines that attempt to understand the reconstruction of inner model parameters, un known nonlinear constitutive relations, heterogeneous structures inside of geological objects, sources or sinks from observational data, - to discuss modern regularization tools for handling improperly posed pro blems and strategies of incorporating a priori knowledge from the applied problem into the model and its treatment. These proceedings contain some of the results of the workshop, representing a bal anced selection of contributions from the various groups of participants. The reviewed invited and contributed articles are grouped according to the broad headings of hydrology, non-linear diffusion and soil physics, geophysical methods, mathematical analysis of inverse and ill-posed problems and parallel algorithms for inverse problems. Some of the issues adressed by the articles in these proceedings include the rela tion between least squares and direct formulations of inverse problems for partial differential equations, nonlinear regularization, identification of nonlinear consti tutive relations, fast parallel algorithms for large scale inverse problems, reduction of model structures, geostatistical inversion techniques.
Classification Parameter Estimation And State Estimation
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Author : Bangjun Lei
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-03
Classification Parameter Estimation And State Estimation written by Bangjun Lei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-03 with Science categories.
A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods—especially among adaboost varieties and particle filtering methods—have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB—especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.
Classification Parameter Estimation And State Estimation
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Author : Ferdinand van der Heijden
language : en
Publisher: John Wiley & Sons
Release Date : 2005-06-10
Classification Parameter Estimation And State Estimation written by Ferdinand van der Heijden and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-10 with Science categories.
Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis. Covers all contemporary main methods for classification and estimation. Integrated approach to classification, parameter estimation and state estimation Highlights the practical deployment of theoretical issues. Provides a concise and practical approach supported by MATLAB toolbox. Offers exercises at the end of each chapter and numerous worked out examples. PRtools toolbox (MATLAB) and code of worked out examples available from the internet Many examples showing implementations in MATLAB Enables students to practice their skills using a MATLAB environment
Entropy Based Parameter Estimation In Hydrology
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Author : V.P. Singh
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Entropy Based Parameter Estimation In Hydrology written by V.P. Singh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Science categories.
Since the pioneering work of Shannon in the late 1940's on the development of the theory of entropy and the landmark contributions of Jaynes a decade later leading to the development of the principle of maximum entropy (POME), the concept of entropy has been increasingly applied in a wide spectrum of areas, including chemistry, electronics and communications engineering, data acquisition and storage and retreival, data monitoring network design, ecology, economics, environmental engineering, earth sciences, fluid mechanics, genetics, geology, geomorphology, geophysics, geotechnical engineering, hydraulics, hydrology, image processing, management sciences, operations research, pattern recognition and identification, photogrammetry, psychology, physics and quantum mechanics, reliability analysis, reservoir engineering, statistical mechanics, thermodynamics, topology, transportation engineering, turbulence modeling, and so on. New areas finding application of entropy have since continued to unfold. The entropy concept is indeed versatile and its applicability widespread. In the area of hydrology and water resources, a range of applications of entropy have been reported during the past three decades or so. This book focuses on parameter estimation using entropy for a number of distributions frequently used in hydrology. In the entropy-based parameter estimation the distribution parameters are expressed in terms of the given information, called constraints. Thus, the method lends itself to a physical interpretation of the parameters. Because the information to be specified usually constitutes sufficient statistics for the distribution under consideration, the entropy method provides a quantitative way to express the information contained in the distribution.
Identification And Compilation Of Unsaturated Vadose Zone Models
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Author : Paul Van der Heijde
language : en
Publisher:
Release Date : 1994
Identification And Compilation Of Unsaturated Vadose Zone Models written by Paul Van der Heijde and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Groundwater flow categories.
Model Calibration And Parameter Estimation
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Author : Ne-Zheng Sun
language : en
Publisher: Springer
Release Date : 2015-07-01
Model Calibration And Parameter Estimation written by Ne-Zheng Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-01 with Mathematics categories.
This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.
Reservoir Analysis And Parameter Estimation Constrained To Temperature Pressure And Flowrate Histories
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Author : Obinna Onyinye Duru
language : en
Publisher: Stanford University
Release Date : 2011
Reservoir Analysis And Parameter Estimation Constrained To Temperature Pressure And Flowrate Histories written by Obinna Onyinye Duru and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Acquisition of downhole temperature measurements, in addition to production data, is routine in production systems. The temperature measurements, which are currently being used for pressure data correction, are cheap to acquire, accurate and have good resolutions. The answer to the question of how useful these temperature measurements can be, beyond the current utilization for pressure correction, was the goal of this research work. In the first part of this work, a mechanistic multiphysics and multiscale model for thermal transport process in a porous medium was developed, accounting for compressibility and viscous dissipation effects like Joule-Thomson and adiabatic expansion phenomena. To validate the model, a laboratory experiment was designed to allow for a controlled flow of air through a porous core, while measuring the temperature changes at different locations. The data acquired were used to verify the model and perform sensitivity studies, and the results showed functional dependencies of the model on useful reservoir parameters such as porosity, flow velocities and thermal properties of the rock and fluid; and these functional dependencies revealed the potential of temperature data as an additional source of constraining data in temporal and distributed reservoir parameter estimation. In addition, the temperature model was well suited for the application of a number of analytical tools that lead to the extraction of these useful reservoir characteristic information. In the second part, using multiresolution methods based on the second derivative of the Gaussian kernel, temperature measurements were combined with pressure data to improve the identification of transients in data as well as yield better behavioral filtering. Until now, only pressure measurements are used and this has shown to be unreliable. The approach developed here exploited the independence between the pressure and temperature measurements to constrain the estimation of the location of the breakpoints. The third segment of this research exploited the convective nature of thermal transport during flow to characterize near wellbore properties such as the extent of damage around a well (or extent of stimulation). The model lent itself to the application of the semianalytical Operator Splitting decomposition technique and as a result, the solution of the advection component could be separated and used to estimate near-wellbore structures such as damage or stimulation radius and permeability. As temperature measurements are an independent source of measurements, a joint inversion of production data and temporal temperature measurements, taken from multiwell production systems, showed a significant improvement in the reservoir state estimation problem, using state space estimation techniques like the Ensemble Kalman filter. This marked improvement was over the results from current approaches which match only production data. Results showed that introducing temperature improved the resolution of both permeability and porosity fields significantly. The last part of this research dealt with the estimation of flowrate, using only temperature measurements. The temperature model showed a strong functional dependence of temperature on flowrates at high Peclet number. By deconvolution, the advective flow kernel was separated from the diffusion part, and the complete flowrate history reconstructed from this kernel. Results showed that in synthetic and field cases, this extracted flowrate compared well with the true flowrate measurements. The philosophical significance of this work is that low-cost temperature measurements, which are measured routinely in producing wells, are a promising source of additional data for further constraining of reservoir characterization and optimization problems.
Parameter Estimation And Uncertainty Quantification In Water Resources Modeling
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Author : Philippe Renard
language : en
Publisher: Frontiers Media SA
Release Date : 2020-04-22
Parameter Estimation And Uncertainty Quantification In Water Resources Modeling written by Philippe Renard and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-22 with categories.
Numerical models of flow and transport processes are heavily employed in the fields of surface, soil, and groundwater hydrology. They are used to interpret field observations, analyze complex and coupled processes, or to support decision making related to large societal issues such as the water-energy nexus or sustainable water management and food production. Parameter estimation and uncertainty quantification are two key features of modern science-based predictions. When applied to water resources, these tasks must cope with many degrees of freedom and large datasets. Both are challenging and require novel theoretical and computational approaches to handle complex models with large number of unknown parameters.
Entropy Based Parameter Estimation In Hydrology
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Author : Vijay Singh
language : en
Publisher: Springer Science & Business Media
Release Date : 1998-10-31
Entropy Based Parameter Estimation In Hydrology written by Vijay Singh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-10-31 with Science categories.
Since the pioneering work of Shannon in the late 1940's on the development of the theory of entropy and the landmark contributions of Jaynes a decade later leading to the development of the principle of maximum entropy (POME), the concept of entropy has been increasingly applied in a wide spectrum of areas, including chemistry, electronics and communications engineering, data acquisition and storage and retreival, data monitoring network design, ecology, economics, environmental engineering, earth sciences, fluid mechanics, genetics, geology, geomorphology, geophysics, geotechnical engineering, hydraulics, hydrology, image processing, management sciences, operations research, pattern recognition and identification, photogrammetry, psychology, physics and quantum mechanics, reliability analysis, reservoir engineering, statistical mechanics, thermodynamics, topology, transportation engineering, turbulence modeling, and so on. New areas finding application of entropy have since continued to unfold. The entropy concept is indeed versatile and its applicability widespread. In the area of hydrology and water resources, a range of applications of entropy have been reported during the past three decades or so. This book focuses on parameter estimation using entropy for a number of distributions frequently used in hydrology. In the entropy-based parameter estimation the distribution parameters are expressed in terms of the given information, called constraints. Thus, the method lends itself to a physical interpretation of the parameters. Because the information to be specified usually constitutes sufficient statistics for the distribution under consideration, the entropy method provides a quantitative way to express the information contained in the distribution.