Kanevski / Timonin / Pozdnukhov Machine Learning for Spatial Environmental Data

Theory, Applications, and Software
Erscheinungsjahr 2010
ISBN: 978-1-4398-0808-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 400 Seiten

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.

Zielgruppe

Students and PhD students in geographical, geological, and environmental departments; geophysicists, environmentalists (soil sciences, geography, mining), regulatory agencies, and statisticians.

Autoren/Hrsg.

Kanevski, Mikhail

Timonin, Vadim

Pozdnukhov, Alexi

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Geowissenschaften Geologie GIS, Geoinformatik
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Inhaltsverzeichnis

Learning From Geospatial Data: Problems and Important Concepts of Machine Learning – Machine Learning Algorithms for Geospatial Data – Contents of the Book. Software Description – Short Review of the Literature - Exploratory Spatial Data Analysis: Presentation of Data and Case Studies: Exploratory Spatial Data Analysis – Data Pre-Processing – Spatial Correlations: Variography – Presentation of Data – k-Nearest Neighbours Algorithm: a Benchmark Model for Regression and Classification - Geostatistics: Spatial Predictions – Geostatistical Conditional Simulations – Spatial Classification – Software - Machine Learning Algorithms: Artificial Neural Networks: Introduction – Radial Basis Function Neural Networks – General Regression Neural Networks – Probabilistic Neural Networks – Self-Organising Maps – Gaussian Mixture Models And Mixture Density Network • Support Vector Machines And Kernel Methods: Introduction to Statistical Learning Theory – Support Vector Classification – Spatial Data Classification with SVM – Support Vector Regression – Spatial Data Mapping with SVR – Advanced Topics in Kernel Methods.