Deep Learning For Hydrometeorology And Environmental Science
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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.
Modeling And Monitoring Extreme Hydrometeorological Events
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Author : Maftei, Carmen
language : en
Publisher: IGI Global
Release Date : 2024-01-10
Modeling And Monitoring Extreme Hydrometeorological Events written by Maftei, Carmen and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-10 with Science categories.
In a world experiencing increasingly intense hydrometeorological events driven by climate change, the need for effective solutions is paramount. Modeling and Monitoring Extreme Hydrometeorological Events presents a cutting-edge exploration of the challenges posed by flash droughts and floods, offering innovative methodologies and tools to address these global issues. Through a combination of computer modeling, remote sensing, artificial intelligence, and case studies, this book provides a comprehensive framework for understanding and mitigating the impacts of extreme hydrometeorological events. It examines the rapid emergence of flash droughts, which bring devastating consequences to agriculture, water resources, ecosystems, and public health. The book also delves into the complex dynamics of flash floods, exploring their causes, impacts, and potential solutions. With a focus on water management, the book addresses knowledge gaps, provides adaptation and mitigation strategies, and emphasizes the importance of climate change considerations. It aims to empower scientists, policymakers, professionals, and educators to develop effective policies and decision-making frameworks to combat the increasing risks posed by extreme hydrometeorological events. Written by a diverse team of experts in hydrology, hydrometeorology, emergency management, civil engineering, and related fields, this book offers valuable insights and practical tools for researchers, professors, graduate students, policymakers, and professionals.
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-16
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 2021-08-16 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.
Computational Automation For Water Security
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Author : Ashutosh Kumar Dubey
language : en
Publisher: Elsevier
Release Date : 2025-02-27
Computational Automation For Water Security written by Ashutosh Kumar Dubey and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Science categories.
Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future. - Offers a holistic understanding of the application of computational automation in water quality management - Contains practical and unique updates to help learners how to apply computational techniques to address water quality challenges - Provides a comprehensive and multidisciplinary perspective on water quality management
Machine Learning In Earth Environmental And Planetary Sciences
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Author : Hossein Bonakdari
language : en
Publisher: Elsevier
Release Date : 2023-07-03
Machine Learning In Earth Environmental And Planetary Sciences written by Hossein Bonakdari and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Science categories.
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
Machine Learning In Chemical Safety And Health
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Author : Qingsheng Wang
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-21
Machine Learning In Chemical Safety And Health written by Qingsheng Wang 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 2022-10-21 with Technology & Engineering categories.
Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validation, and an overview of software and tools Detailed reviews of various applications in the areas of chemical safety and health, including flammability prediction, consequence prediction, asset integrity management, predictive nanotoxicity and environmental exposure assessment, and more Perspective on the possible future development of this field Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.
Broadening The Use Of Machine Learning In Hydrology
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Author : Chaopeng Shen
language : en
Publisher: Frontiers Media SA
Release Date : 2021-07-08
Broadening The Use Of Machine Learning In Hydrology written by Chaopeng Shen 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 2021-07-08 with Science categories.
Computational Intelligence For Water And Environmental Sciences
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Author : Omid Bozorg-Haddad
language : en
Publisher: Springer Nature
Release Date : 2022-07-08
Computational Intelligence For Water And Environmental Sciences written by Omid Bozorg-Haddad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-08 with Technology & Engineering categories.
This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.
Hydrometeorology
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Author : Kevin Sene
language : en
Publisher: Springer Nature
Release Date : 2024-07-05
Hydrometeorology written by Kevin Sene and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-05 with Science categories.
An award-winning text introducing the latest operational hydrometeorological forecasting and warning techniques for flood, drought, reservoir, hydropower, irrigation, water supply and water pollution applications. Hydrometeorology: Forecasting and Applications is the latest edition of this award-winning book intended for practicing engineers and scientists. It also provides useful background for undergraduate and postgraduate courses in engineering, earth sciences, environmental sciences, geography, meteorology and hydrology. Operational examples include applications from the USA, UK, the Netherlands, Bangladesh and Nepal. Throughout, there is a focus on end-to-end warning systems, forecast uncertainty and risk-based and impact-based approaches. Hydrological forecasting topics include rainfall-runoff, flow routing, data assimilation, forecast verification and ensemble techniques. There are also updates to the text on weather radar, satellite precipitation estimates, hydrometry, low cost monitoring, numerical weather prediction, demand forecasting and dissemination of warnings, including the role of social media and citizen science. Applications include national and community based flood warning systems, flash flood guidance, famine and drought early warning systems, reservoir operations, and surface water, debris flow, ice jam, bathing water and harmful algal bloom alerts. Seasonal forecasting, land surface and global hydrological models are now discussed in more detail, including the opportunities from ‘Big Data’ and artificial intelligence, and a new chapter discusses approaches to predicting the hydrological impacts of climate change. The extensive sets of references have been revised and updated. “There are few books that have ever attempted to discuss all aspects of hydrometeorology in one volume. This one does so.” From a review of the 1st edition in the Bulletin of the American Meteorological Society. Kevin Sene FRGS, FRMetS, CEng (MICE) is a scientist and writer with wide experience in flood risk management, water resources and hydrometeorology. His previous publications include books on flood warning, forecasting and emergency response and flash flood forecasting and warning (Springer, 2008, 2013).
Multisensor Data Fusion And Machine Learning For Environmental Remote Sensing
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Author : Ni-Bin Chang
language : en
Publisher: CRC Press
Release Date : 2018-02-21
Multisensor Data Fusion And Machine Learning For Environmental Remote Sensing written by Ni-Bin Chang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Technology & Engineering categories.
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.