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Author: Angela Gorgoglione
Publisher: MDPI
ISBN: 3039282107
Category : Science
Languages : en
Pages : 94
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Book Description
heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.
Author: Angela Gorgoglione
Publisher: MDPI
ISBN: 3039282107
Category : Science
Languages : en
Pages : 94
Get Book
Book Description
heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.
Author: Angela Gorgoglione
Publisher:
ISBN: 9783039282111
Category : Engineering (General). Civil engineering (General)
Languages : en
Pages : 94
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Book Description
Heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response's complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.
Author: Laurens M. Bouwer
Publisher: Springer Nature
ISBN: 3030995461
Category : Science
Languages : en
Pages : 158
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Book Description
This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
Author: Benedetto Nastasi
Publisher: MDPI
ISBN: 3039362186
Category : Science
Languages : en
Pages : 218
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Book Description
Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.
Author: Philippe Gourbesville
Publisher: Springer Nature
ISBN: 9811554366
Category : Science
Languages : en
Pages : 1056
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Book Description
This book features a collection of extended papers based on presentations given at the SimHydro 2019 conference, held in Sophia Antipolis in June 2019 with the support of French Hydrotechnic Society (SHF), focusing on “Which models for extreme situations and crisis management?” Hydraulics and related disciplines are frequently applied in extreme situations that need to be understood accurately before implementing actions and defining appropriate mitigation measures. However, in such situations currently used models may be partly irrelevant due to factors like the new physical phenomena involved, the scale of the processes, and the hypothesis included in the different numerical tools. The availability of computational resources and new capacities like GPU offers modellers the opportunity to explore various approaches to provide information for decision-makers. At the same time, the topic of crisis management has sparked interest from stakeholders who need to share a common understanding of a situation. Hydroinfomatics tools can provide essential information in crises; however, the design and integration of models in decision-support systems require further development and the engagement of various communities, such as first responders. In this context, methodologies, guidelines and standards are more and more in demand in order to ensure that the systems developed are efficient and sustainable. Exploring both the limitations and performance of current models, this book presents the latest developments based on new numerical schemes, high-performance computing, multiphysics and multiscale methods, as well as better integration of field-scale model data. As such, it will appeal to practitioners, stakeholders, researchers and engineers active in this field.
Author: Ashok N. Srivastava
Publisher: CRC Press
ISBN: 1498703887
Category : Computers
Languages : en
Pages : 208
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Book Description
From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
Author:
Publisher:
ISBN:
Category : Earth sciences
Languages : en
Pages : 46
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Book Description
Author: Hassane Jarar Oulidi
Publisher: Springer Nature
ISBN: 3030249743
Category : Science
Languages : en
Pages : 111
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Book Description
This book aims to exchange and share the experiences and research results on the geospatial technology applied in water resources management. It will present the most recent innovations, trends, challenges encountered and the solutions adopted in the area of geospatial technology. It will be beneficial for academicians, scientists, meteorologists, and consultants working in the field of water resources management.
Author:
Publisher: ScholarlyEditions
ISBN: 146496338X
Category : Science
Languages : en
Pages : 6804
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Book Description
Issues in Earth Sciences, Geology, and Geophysics: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Earth Sciences, Geology, and Geophysics. The editors have built Issues in Earth Sciences, Geology, and Geophysics: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Earth Sciences, Geology, and Geophysics in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Earth Sciences, Geology, and Geophysics: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Author: Graham J. Borradaile
Publisher: Springer Science & Business Media
ISBN: 9783540436034
Category : Mathematics
Languages : en
Pages : 388
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Book Description
From the reviews: "All in all, Graham Borradaile has written and interesting and idiosyncratic book on statistics for geoscientists that will be welcome among students, researchers, and practitioners dealing with orientation data. That should include engineering geologists who work with things like rock fracture orientation measurements or clast alignment in paleoseismic trenches. It won’t replace the collection of statistics and geostatistics texts in my library, but it will have a place among them and will likely be one of several references to which I turn when working with orientation data.... The text is easy to follow and illustrations are generally clear and easy to read..."(William C. Haneberg, Haneberg Geoscience)
