Presses polytechniques et universitaires romandes
20181303
eng
www.ppur.org/518/9782940222025
03
01
Presses polytechniques et universitaires romandes
03
9782940222025
15
9782940222025
BA
Environmental Engineering
<TitleType>01</TitleType>
<TitleText>Analysis and Modelling of Spatial Environmental Data</TitleText>
1
A01
Mikhail
Kanevski
<p>Prof. Mikhail Kanevski is currently Professor at the institute of Geomatics and Analysis of Risk (IGAR) at the University of Lausanne. Current reserch interest include the development and applications of machine learning algorithms for spatio-temporal data analysis, modelling and visualization; geostatistics and geosimulations; natural hazards and environmental risks assesments; environmental, socio-economic and and financial time series analysis and prediction.</p>
2
A01
Michel
Maignan
NED
1
01
eng
306
23
Environmental Engineering
23
Environnement
01
02
<p>This book describes the fundamental methodological aspects of the analysis and modelling of spacially distributed data, and the applications with the specific userfriendly software Geostat Office. The methods presented in this book include two domains of geostatistics and of machine learning algorithms, and some aspects of Geographical Information Systems. The geostatistical methods cover the traditionnal variography and spatial predictions, as well as an extensive part on conditional stochastic simulations and estimation of local probability distribution functions. A special chapter is devoted to the exploratory spatial data analysis, where the analysis of monitoring network is extensively decribed. In addition to more traditional geostatistics, the methods of artificial neural networks of different architectures ans Support Vector Machines (SVM) are explained ans illustrated. The key feature of machine learning algorithms is that learn from data and can be efficiently used when the modelled phenomenon is not described accurately. Machine Learning algorithms are adaptive tools to solve prediction, characterization, optimisation and density estimation problems. The fundamentals of Statistical Learning Theory (Vapnik-Chervonenkis theory) is explained using examples of real environmental spatial data; SVM develop robust data models with good generalisation capabilities. The book is distributed with the student version of Geostat Office Software which runs under Microsoft Windows. The book and its GSO software can be useful for teaching as well as for modelling real case studies.::This book may be ordered by customers located in Switzerland, France,Belgium and North Africa only. For other countries, please contactDekker Ltd. at www.dekker.com::</p>
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Preface Introduction to environmental data analysis and modelling Exploratory spatial data analysis. Analysis of monitoring networks. Declustering Spatial data analysis: deterministic interpolations Introduction to Geostatistics. Variography Geostatistical spatial predictions Estimation of local probability density functions Conditional stochastic simulations Artificial neural networks and spatial data analysis Support Vector Machines for environmental spatial data Geographical Information Systems and spatial data analysis Conclusions Glossaries References.
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This book describes the fundamental methodological aspects of the analysis and modelling of spacially distributed data, and the applications with the specific userfriendly software Geostat Office. The methods presented in this book include two domains of geostatistics and of machine learning algorithms, and some aspects of Geographical Information Systems. The geostatistical methods cover the traditionnal variography and spatial predictions, as well as an extensive part on conditional stochastic simulations and estimation of local probability distribution functions. A special chapter is devoted to the exploratory spatial data analysis, where the analysis of monitoring network is extensively decribed. In addition to more traditional geostatistics, the methods of artificial neural networks of different architectures ans Support Vector Machines (SVM) are explained ans illustrated. The key feature of machine learning algorithms is that learn from data and can be efficiently used when the modelled phenomenon is not described accurately. Machine Learning algorithms are adaptive tools to solve prediction, characterization, optimisation and density estimation problems. The fundamentals of Statistical Learning Theory (Vapnik-Chervonenkis theory) is explained using examples of real environmental spatial data; SVM develop robust data models with good generalisation capabilities. The book is distributed with the student version of Geostat Office Software which runs under Microsoft Windows. The book and its GSO software can be useful for teaching as well as for modelling real case studies.::This book may be ordered by customers located in Switzerland, France,Belgium and North Africa only. For other countries, please contactDekker Ltd. at www.dekker.com::
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Presses polytechniques et universitaires romandes
http://www.ppur.org/produit/518/9782940222025/Analysis%20and%20Modelling%20of%20Spatial%20Environmental%20Data
28
http://www.ppur.org/store/preview/518
29
http://www.ppur.org/produit/518/9782940222025/Analysis%20and%20Modelling%20of%20Spatial%20Environmental%20Data
01
EPFL Press
CH
04
20040302
01
240.0
mm
02
160.0
mm
Presses polytechniques et universitaires romandes
01
20
02
02
90.05
EUR
S
2.50
87.85
2.20
02
02
113.00
CHF
S
2.50
110.24
2.76