Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results. - Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data - Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes - Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives. Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences. - Facilitates the application of artificial intelligence and data science systems to create comprehensive methodologies for analyzing, processing, predicting, and management strategies in the fields of Earth, environment, and planetary science - Developed with an interdisciplinary framework, with an aim to promote artificial intelligence models for real-time Earth systems - Includes a section on case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives

Many facets of studies in planetary science are dependent on analyzing large volumes of in-situ and spacecraft data. Our understanding of the lunar evolution and its connection to deeper interior layers has been dramatically improved by the so-called deep space exploration missions, including NASA’s lunar reconnaissance orbiter narrow-angle camera, China’s Moon and Mar exploration program, and Chang’e series lunar relay satellite program, in coordination with Earth-based supporting observations. From Apollo 17 lunar surface operations (1972), there are many related operations including China’s lunar and deep space exploration (Chang’e-1 2007; Chang’e-2 2010; Chang’e-3 2013; Chang’e-4 2018; Chang’e-5 2020). On the 50th anniversary of the final Apollo mission to the Moon, this topic will bring together theory, numerical models, and observations capable of advancing our understanding of lunar evolution.

Research institutes, foundations, centers, bureaus, laboratories, experiment stations, and other similar nonprofit facilities, organizations, and activities in the United States and Canada. Entry gives identifying and descriptive information of staff and work. Institutional, research centers, and subject indexes. 5th ed., 5491 entries; 6th ed., 6268 entries.

The six volumes of Peterson's Annual Guides to Graduate Study, the only annually updated reference work of its kind, provide wide-ranging information on the graduate and professional programs offered by accredited colleges and universities in the United States and U.S. territories and those in Canada, Mexico, Europe, and Africa that are accredited by U.S. accrediting bodies. Books 2 through 6 are divided into sections that contain one or more directories devoted to individual programs in a particular field. Book 4 contains more than 3,800 programs of study in 56 disciplines of the physical sciences, mathematics, agricultural sciences, the environment, and natural resources.