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.

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

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.

Photovoltaic technology has now developed to the extent that it is close to fulfilling the vision of a “solar-energy world,” as devices based on this technology are becoming efficient, low-cost and durable. This book provides a comprehensive treatment of thin-film silicon, the most prevalent PV material, in terms of its semiconductor nature, starting out with the physical properties, but concentrating on device applications.

The use of neural implants for stimulation and recording show excellent promise in restoring certain functions to the central nervous system; and neuroprostheses remain one of the most important tools of neuroscientists for the elucidation of the brain’s function.

Barely five weeks after the opening of the Rolex Learning Center, the verdict fell—the building’s architects, Kazuyo Sejima and Ryue Nishizawa (SAANA), were announced the winners of the 2010 Pritzker Prize, the most prestigious prize in architecture.

The dye-sensitized solar cell (DSC) is a photovoltaic converter that mimics natural photosynthesis. Like green plants and algae it uses a molecular absorber, the dye, to harvest sunlight and generate electric charges.