Machine Learning for Spatial Environmental Data PDF Download
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Author: Mikhail Kanevski
Publisher: EPFL Press
ISBN: 9780849382376
Category : Science
Languages : en
Pages : 444
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Book Description
Acompanyament de CD-RM conté MLO software, la guia d'MLO (pdf) i exemples de dades.
Author: Mikhail Kanevski
Publisher: EPFL Press
ISBN: 9780849382376
Category : Science
Languages : en
Pages : 444
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Book Description
Acompanyament de CD-RM conté MLO software, la guia d'MLO (pdf) i exemples de dades.
Author: Mikhail Kanevski
Publisher: CRC Press
ISBN: 0849382378
Category : Computers
Languages : en
Pages : 384
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Book Description
This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.
Author: Mikhail Kanevski
Publisher:
ISBN: 9782940222247
Category : Cartography
Languages : en
Pages : 377
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Book Description
Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.
Author: Mikhail Kanevski
Publisher: EPFL Press
ISBN: 9780824759810
Category : Technology & Engineering
Languages : en
Pages : 312
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Book Description
Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, and select aspects of Geographical Information Systems. It includes real case studies using Geostat Office software tools under MS Windows and also provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation. The author describes fundamental methodological aspects of the analysis and modelling of spatially distributed data and the application by way of a specific and user-friendly software, GSO Geostat Office. Presenting complete coverage of geostatistics and machine learning algorithms, the book explores the relationships and complementary nature of both approaches and illustrates them with environmental and pollution data. The book includes introductory chapters on machine learning, artificial neural networks of different architectures, and support vector machines algorithms. Several chapters cover monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book demonstrates thepromising results of the application of SVM to environmental and pollution data.
Author: Mikhail Kanevski
Publisher: John Wiley & Sons
ISBN: 1118623266
Category : Social Science
Languages : en
Pages : 224
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Book Description
This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.
Author: Alan H. Fielding
Publisher: Springer Science & Business Media
ISBN: 1461552893
Category : Science
Languages : en
Pages : 265
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Book Description
This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
Author: William W. Hsieh
Publisher: Cambridge University Press
ISBN: 0521791928
Category : Computers
Languages : en
Pages : 364
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Book Description
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Author: Jerry Davis
Publisher: CRC Press
ISBN: 100084241X
Category : Business & Economics
Languages : en
Pages : 492
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Book Description
Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics and modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels. Features • Gives thorough consideration of the needs for environmental research in both spatial and temporal domains. • Features examples of applications involving field-collected data ranging from individual observations to data logging. • Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA. • Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors. • All examples and exercises make use of a GitHub package for functions and especially data.
Author: Sue Ellen Haupt
Publisher: Springer Science & Business Media
ISBN: 1402091192
Category : Science
Languages : en
Pages : 418
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Book Description
How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
Author: Ni-Bin Chang
Publisher: CRC Press
ISBN: 1351650637
Category : Technology & Engineering
Languages : en
Pages : 647
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Book Description
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
