Machine Learning for Spatial Environmental Data: Theory, Applications and Software - Environmental Sciences | Paperback | 392 Pages

Explore the cutting edge of machine learning applied to environmental data with this comprehensive guide. This book delves into advanced algorithms and their practical implementation for classifying and mapping spatially distributed environmental information.

Key Features:

• State-of-the-art machine learning algorithms including artificial neural networks and support vector machines.
• Application of algorithms to the classification and mapping of spatially distributed environmental data.
• Presentation of basic geostatistical algorithms.
• Exploration of new trends in machine learning and their application to spatial data.
• Real case studies based on environmental and pollution data.
• Includes a CD-ROM with Machine Learning Office software for hands-on practice.

What's Included:

• Machine Learning for Spatial Environmental Data: Theory, Applications and Software (Environmental Sciences) book
• CD-ROM with Machine Learning Office software and sample data sets

Specifications:

• Authors: Mikhail Kanevski, Alexei Pozdnoukhov, Vadim Timonin
• Publisher: Presses Polytechniques et Universitaires Romandes
• Publication Date: September 10, 2026
• Language: English
• Print Length: 392 pages
• Binding: Paperback
• Dimensions: 159 x 28 x 241 mm (6.26 x 1.1 x 9.49 inches)
• Weight: 454g (1 lb)
• ISBN-10: 294022224X
• ISBN-13: 978-2940222247
• Series: Environmental Sciences

This book is an invaluable resource for students and researchers seeking to apply advanced machine learning techniques to environmental challenges.