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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
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
Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics
How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
Incorporating historical, sociological, and philosophical approaches, Changing the Atmosphere presents detailed empirical studies of climate science and its uptake into public policy.
Now in its 46th edition, British Qualifications is the definitive one-volume guide to every qualification on offer in the United Kingdom. With an equal focus on vocational studies, this essential guide has full details of all institutions and organizations involved in the provision of further and higher education and is an essential reference source for careers advisors, students and employers. It also includes a comprehensive and up-to-date description of the structure of further and higher education in the UK. The book includes information on awards provided by over 350 professional institutions and accrediting bodies, details of academic universities and colleges and a full description of the current framework of academic and vocational education. It is compiled and checked annually to ensure accuracy of information.
This text discusses sensitivity parametric analysis for the single tuned filter parameters and presents an optimization-based method for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. It also highlights the importance of artificial intelligence techniques such as water cycle algorithms in solving power quality problems such as over-voltage and harmonic distortion. Features: Presents a sensitivity parametric analysis for the single tuned filter parameters. Discusses optimization-based methods for solving the allocation problem of the distributed generation units and capacitor banks in distribution systems. Highlights the importance of artificial intelligence techniques (water cycle algorithm) for solving power quality problems such as over-voltage and harmonic distortion. Showcases a procedure for harmonic mitigation in active distribution systems using the single tuned harmonic filters. Helps in learning how to determine the optimal planning of the single tuned filters to mitigate the harmonic distortion in distorted systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, Power systems planning and analysis.