Machine Learning for Spatial Environmental Data

Name: Machine Learning for Spatial Environmental Data
Brand: EPFL Press English Imprint
SKU: 9782940222247
Price: 160.0 CHF
Availability: InStock
ISBN: 978-2-94022-224-7

Theory, Applications and Software

de Mikhail Kanevski (auteur), Alexei Pozdnoukhov (auteur), Vadim Timonin (auteur)

Collection : Environmental Engineering

mars 2023

Livre papier + eBook [PDF]	248,34 € 153,27 €
Livre papier format 160 x 240 392 pages Indisponible	139,34 €
eBook [PDF] 392 pages Téléchargement après achat	109,00 €

Medias

Présentation

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Sommaire

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.