Machine Learning For Earth Sciences
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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.
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.
Introduction To Python In Earth Science Data Analysis
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Author : Maurizio Petrelli
language : en
Publisher: Springer Nature
Release Date : 2021-09-16
Introduction To Python In Earth Science Data Analysis written by Maurizio Petrelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Science categories.
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
Machine Learning And Artificial Intelligence In Geosciences
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Author :
language : en
Publisher: Academic Press
Release Date : 2020-09-22
Machine Learning And Artificial Intelligence In Geosciences written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-22 with Science categories.
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
Deep Learning For Hydrometeorology And Environmental Science
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Author : Taesam Lee
language : en
Publisher: Springer Nature
Release Date : 2021-01-27
Deep Learning For Hydrometeorology And Environmental Science written by Taesam Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Science categories.
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Machine Learning For Earth Sciences
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Author : Maurizio Petrelli
language : en
Publisher: Springer Nature
Release Date : 2023-09-22
Machine Learning For Earth Sciences written by Maurizio Petrelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-22 with Science categories.
This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals.
Machine Learning Techniques Applied To Geoscience Information System And Remote Sensing
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Author : Hyung-Sup Jung
language : en
Publisher:
Release Date : 2019
Machine Learning Techniques Applied To Geoscience Information System And Remote Sensing written by Hyung-Sup Jung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic books categories.
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.
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.
Advanced Time Series Analysis In Geosciences
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Author : Flavio Cannavo’
language : en
Publisher: Frontiers Media SA
Release Date : 2021-05-12
Advanced Time Series Analysis In Geosciences written by Flavio Cannavo’ and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-12 with Science categories.