Machine Learning For Spatial Environmental Data
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Machine Learning For Spatial Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher: EPFL Press
Release Date : 2009-06-09
Machine Learning For Spatial Environmental Data written by Mikhail Kanevski and has been published by EPFL Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-09 with Science categories.
Acompanyament de CD-RM conté MLO software, la guia d'MLO (pdf) i exemples de dades.
Machine Learning For Spatial Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher: CRC Press
Release Date : 2009-06-09
Machine Learning For Spatial Environmental Data written by Mikhail Kanevski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-09 with Computers categories.
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 lea
Machine Learning For Spatial Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher:
Release Date : 2009
Machine Learning For Spatial Environmental Data written by Mikhail Kanevski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Cartography categories.
Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.
Analysis And Modelling Of Spatial Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher: EPFL Press
Release Date : 2004-03-30
Analysis And Modelling Of Spatial Environmental Data written by Mikhail Kanevski and has been published by EPFL Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-30 with Technology & Engineering categories.
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.
Deep Learning For Hydrometeorology And Environmental Science
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Author : Taesam Lee
language : en
Publisher: Springer Nature
Release Date : 2021-01-27
Deep Learning For Hydrometeorology And Environmental Science written by Taesam Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Science categories.
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Machine Learning Methods In The Environmental Sciences
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Author : William W. Hsieh
language : en
Publisher: Cambridge University Press
Release Date : 2009-07-30
Machine Learning Methods In The Environmental Sciences written by William W. Hsieh and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-30 with Computers categories.
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Advanced Mapping Of Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-10
Advanced Mapping Of Environmental Data written by Mikhail Kanevski 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 2013-05-10 with Social Science categories.
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.
On Spatio Temporal Data Modelling And Uncertainty Quantification Using Machine Learning And Information Theory
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Author : Fabian Guignard
language : en
Publisher: Springer Nature
Release Date : 2022-03-12
On Spatio Temporal Data Modelling And Uncertainty Quantification Using Machine Learning And Information Theory written by Fabian Guignard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-12 with Science categories.
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.
Computational And Machine Learning Tools For Archaeological Site Modeling
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Author : Maria Elena Castiello
language : en
Publisher: Springer Nature
Release Date : 2022-01-24
Computational And Machine Learning Tools For Archaeological Site Modeling written by Maria Elena Castiello and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-24 with Technology & Engineering categories.
This book describes a novel machine-learning based approach to answer some traditional archaeological problems, relating to archaeological site detection and site locational preferences. Institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva and Fribourg) have been analyzed with an original conceptual framework based on the Random Forest algorithm. It is shown how the algorithm can assist in the modelling process in connection with heterogeneous, incomplete archaeological datasets and related cultural heritage information. Moreover, an in-depth review of past and more recent works of quantitative methods for archaeological predictive modelling is provided. The book guides the readers to set up their own protocol for: i) dealing with uncertain data, ii) predicting archaeological site location, iii) establishing environmental features importance, iv) and suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.
Gis And Machine Learning For Small Area Classifications In Developing Countries
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Author : Adegbola Ojo
language : en
Publisher: CRC Press
Release Date : 2020-12-29
Gis And Machine Learning For Small Area Classifications In Developing Countries written by Adegbola Ojo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-29 with Science categories.
Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers.