Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. (source: Nielsen Book Data)
Vulnerability assessing contagion risk of Covid-19 using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): Case study Chetumal, Mexico.- Land subsidence in Villahermosa Tabasco Mexico, using Radar Interferometry.- A proposal for semantic integration of crime data in Mexico City.- Crowdsourcing for Sargassum monitoring along of the Quintana Roo beaches.- The Air Quality and the relationship with the community birds from the Sierra de Guadalupe.- Swift UI and their integration to Mapkit technology as a framework for representing spatial information in mobile applications.- Forecasting the influx of people on Metrobus line 1 using a fractal analysis.- Mapping biogas from municipal waste, as potential clean energy areas in central Mexico, using Geographic Information Systems.- Geographic Information Systems for Forest Species Distribution and Habitat Suitability.- Index of Coastal Urban Resilience (ICURHF) when coping with Hurricanes and Floods in the City of Chetumal, in the south east of Mexico.- Grouping mixed documents: Mexico job offers case study.
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This book constitutes the refereed proceedings of the First GIS LATAM Conference, GIS LATAM 2020, held in September 2020. Due to the COVID-19 pandemic the conference was held online. The 9 full papers and 2 short papers were thoroughly reviewed and selected from 29 submissions. The papers are focused on the GIS applications in data analytics in spheres of health, environment, government, public, and education. . (source: Nielsen Book Data)