Machine Learning Methods In The Environmental Sciences
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Machine Learning Methods In The Environmental Sciences
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Author : William W. Hsieh
language : en
Publisher: Cambridge University Press
Release Date : 2009-07-30
Machine Learning Methods In The Environmental Sciences written by William W. Hsieh and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-30 with Computers categories.
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Machine Learning Methods In The Environmental Sciences
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Author : William Wei Hsieh
language : en
Publisher:
Release Date : 2014-05-14
Machine Learning Methods In The Environmental Sciences written by William Wei Hsieh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Environmental sciences categories.
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Artificial Intelligence Methods In The Environmental Sciences
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Author : Sue Ellen Haupt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-28
Artificial Intelligence Methods In The Environmental Sciences written by Sue Ellen Haupt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-28 with Science categories.
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.
Machine Learning For Spatial Environmental Data
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Author : Mikhail Kanevski
language : en
Publisher: CRC Press
Release Date : 2009-06-09
Machine Learning For Spatial Environmental Data written by Mikhail Kanevski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-09 with Computers categories.
This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine lea
Computers In Earth And Environmental Sciences
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Author : Hamid Reza Pourghasemi
language : en
Publisher: Elsevier
Release Date : 2021-09-22
Computers In Earth And Environmental Sciences written by Hamid Reza Pourghasemi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Science categories.
Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. - Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences - Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose - Expansively covers specific future challenges in the use of computers in Earth and Environmental Science - Includes case studies that detail the applications of the discussed technologies down to individual hazards
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-18
Deep Learning For The Earth Sciences written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Technology & Engineering categories.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Reshaping Environmental Science Through Machine Learning And Iot
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Author : Gupta, Rajeev Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-06
Reshaping Environmental Science Through Machine Learning And Iot written by Gupta, Rajeev Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Technology & Engineering categories.
In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).
Artificial Intelligence And Machine Learning Methods In Covid 19 And Related Health Diseases
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Author : Victor Chang
language : en
Publisher: Springer Nature
Release Date : 2022-06-28
Artificial Intelligence And Machine Learning Methods In Covid 19 And Related Health Diseases written by Victor Chang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Technology & Engineering categories.
This Springer book provides a perfect platform to submit chapters that discuss the prospective developments and innovative ideas in artificial intelligence and machine learning techniques in the diagnosis of COVID-19. COVID-19 is a huge challenge to humanity and the medical sciences. So far as of today, we have been unable to find a medical solution (Vaccine). However, globally, we are still managing the use of technology for our work, communications, analytics, and predictions with the use of advancement in data science, communication technologies (5G & Internet), and AI. Therefore, we might be able to continue and live safely with the use of research in advancements in data science, AI, machine learning, mobile apps, etc., until we can find a medical solution such as a vaccine. We have selected eleven chapters after the vigorous review process. Each chapter has demonstrated the research contributions and research novelty. Each group of authors must fulfill strict requirements.
Machine Learning
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Author : Peter Flach
language : en
Publisher: Cambridge University Press
Release Date : 2012-09-20
Machine Learning written by Peter Flach and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-20 with Computers categories.
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods
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Author : Chris Aldrich
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-15
Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods written by Chris Aldrich and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-15 with Computers categories.
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.