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Author : Chaopeng Shen,Eric Laloy,Xingyuan Chen
Publisher : Frontiers Media SA
Page : 163 pages
File Size : 50,6 Mb
Release : 2021-07-08
Category : Science
ISBN : 9782889669820
Author : M. Z. Naser
Publisher : John Wiley & Sons
Page : 610 pages
File Size : 49,9 Mb
Release : 2023-07-17
Category : Technology & Engineering
ISBN : 9781119897613
Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.
Author : Adam E. Ahmed,Jameel M. Al-Khayri,Azharia A. Elbushra
Publisher : Springer Nature
Page : 500 pages
File Size : 55,8 Mb
Release : 2024-01-31
Category : Technology & Engineering
ISBN : 9783031467042
Food and nutrition security is a major concern for Saudi Arabia and the surrounding regions due to the range of challenges they face. These challenges include limited agricultural resources, low self-sufficiency in key food staples, climate change, and high levels of food loss and waste. This book aims to evaluate and analyze the current situation and future prospects of food and nutrition security in Saudi Arabia. Additionally, it seeks to analyze and assess the roles and functions of various institutions related to food security, providing a deeper understanding of the complex problems associated with it. Furthermore, this book aligns with Kingdom Vision 2030, which includes a set of strategies and programs focused on agriculture, food, and water security. It also aligns with the institutional identity of King Faisal University's "Food Security and Environmental Sustainability". The book consists of four volumes. Volume 2 is entitled "Macroeconomic Policy Implications on Food and Nutrition Security". It covers various areas, including food price, loss and waste, processing, finance, trade, investment, quality and safety, consumption patterns, climate change, early warning systems, nutrition institutions, oil revenue, and the significance of date palm and Hassawi rice, genetically modified food, and edible insects in ensuring food and nutritional security. This book is highly significant for professionals, researchers, policymakers, and entrepreneurs involved in food and nutrition security in Saudi Arabia, the Gulf Cooperation Council, and various national and international organizations. It offers a comprehensive analysis of the obstacles and possibilities in ensuring food and nutrition security, as well as presenting practical approaches to address these issues. Additionally, graduate students studying in fields related to food and nutrition security will benefit from this book.
Author : Gerald A. Corzo Perez,Dimitri P. Solomatine
Publisher : John Wiley & Sons
Page : 483 pages
File Size : 41,6 Mb
Release : 2023-12-12
Category : Science
ISBN : 9781119639343
Advanced Hydroinformatics Advanced Hydroinformatics Machine Learning and Optimization for Water Resources The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management. Volume Highlights Include: Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics Advances in modeling hydrological systems Different data analysis methods and models for forecasting water resources New areas of knowledge discovery and optimization based on using machine learning techniques Case studies from North America, South America, the Caribbean, Europe, and Asia The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Author : Saeid Eslamian,Faezeh Eslamian
Publisher : Elsevier
Page : 484 pages
File Size : 44,5 Mb
Release : 2022-11-30
Category : Technology & Engineering
ISBN : 9780128219706
Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series.? Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc.?It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. Introduces classic soft-computing techniques, necessary for a range of disciplines.
Author : Renji Remesan,Jimson Mathew
Publisher : Springer
Page : 250 pages
File Size : 51,9 Mb
Release : 2014-11-03
Category : Science
ISBN : 9783319092355
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Author : Roshan Karan Srivastav,Purna C. Nayak
Publisher : Springer
Page : 0 pages
File Size : 55,8 Mb
Release : 2024-10-24
Category : Mathematics
ISBN : 3031644026
Applications of Machine Learning in Hydroclimatology is a comprehensive exploration of the transformative potential of machine learning for addressing critical challenges in water resources management. The book explores how artificial intelligence can unravel the complexities of hydrological systems, providing researchers and practitioners with cutting-edge tools to model, predict, and manage these systems with greater precision and effectiveness. It thoroughly examines the modeling of hydrometeorological extremes, such as floods and droughts, which are becoming increasingly difficult to predict due to climate change. By leveraging AI-driven methods to forecast these extremes, the book offers innovative approaches that enhance predictive accuracy. It emphasizes the importance of analyzing non-stationarity and uncertainty in a rapidly evolving climate landscape, illustrating how statistical and frequency analyses can improve hydrological forecasts. Moreover, the book explores the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management. To provide practical solutions, the book includes case studies that showcase effective mitigation measures for water-related challenges. These examples highlight the use of machine learning techniques such as deep learning, reinforcement learning, and statistical downscaling in real-world scenarios. They demonstrate how artificial intelligence can optimize decision-making and resource management while improving our understanding of complex hydrological phenomena. By utilizing machine learning architectures tailored to hydrology, the book presents physics-guided models, data-driven techniques, and hybrid approaches that can be used to address water management issues. Ultimately, Applications of Machine Learning in Hydroclimatology empowers researchers, practitioners, and policymakers to harness machine learning for sustainable water management. It bridges the gap between advanced AI technologies and hydrological science, offering innovative solutions to tackle today's most pressing challenges in water resources.
Author : Sandeep Samantaray,Abinash Sahoo,Dillip K. Ghose
Publisher : CRC Press
Page : 310 pages
File Size : 42,9 Mb
Release : 2021-05-16
Category : Technology & Engineering
ISBN : 9781000386738
Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.
Author : Taesam Lee,Vijay P. Singh,Kyung Hwa Cho
Publisher : Unknown
Page : 0 pages
File Size : 41,8 Mb
Release : 2021
Category : Electronic
ISBN : 3030647781
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.
Author : Durga Lal Shrestha
Publisher : CRC Press
Page : 224 pages
File Size : 52,8 Mb
Release : 2010-01-15
Category : Science
ISBN : 0415565987
This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model’s performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.
Author : A.J. Tallón-Ballesteros
Publisher : IOS Press
Page : 738 pages
File Size : 43,5 Mb
Release : 2021-12-03
Category : Computers
ISBN : 9781643682259
It is data that guides the path of applications, and Big Data technologies are enabling new paths which can deal with information in a reasonable time to arrive at an approximate solution, rather than a more exact result in an unacceptably long time. This can be particularly important when dealing with an urgent issue such as that of the COVID-19 pandemic. This book presents the proceedings of two conferences: MMBD 2021 and MLIS 2021. The MMBD conference deals with two main subjects; those of Big Data and Modern Management. The MLIS conference aims to provide a platform for knowledge exchange of the most recent scientific and technological advances in the field of machine learning and intelligent systems. Both conferences were originally scheduled to be held from 8-11 November 2021, in Quanzhou, China and Xiamen, China respectively. Both conferences were ultimately held fully online on the same dates, hosted by Huaqiao University in Quanzhou and Xiamen respectively. The book is in two parts, and contains a total of 78 papers (54 from MMBD2021 and 24 from MLIS2021) selected after rigorous review from a total of some 300 submissions. The reviewers bore in mind the breadth and depth of the research topics that fall within the scope of MMBD and MLIS, and selected the 78 most promising and FAIA mainstream-relevant contributions for inclusion in this two-part volume. All the papers present original ideas or results of general significance supported by clear reasoning, compelling evidence and rigorous methods.
Author : Maftei, Carmen,Muntean, Radu,Vaseashta, Ashok
Publisher : IGI Global
Page : 359 pages
File Size : 53,9 Mb
Release : 2024-01-10
Category : Science
ISBN : 9781668487730
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.
Author : Syed Nisar Hussain Bukhari
Publisher : CRC Press
Page : 301 pages
File Size : 52,7 Mb
Release : 2024-06-13
Category : Computers
ISBN : 9781040037232
In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Author : University of Michigan
Publisher : UM Libraries
Page : 212 pages
File Size : 50,6 Mb
Release : 1999
Category : Education, Higher
ISBN : UOM:39015078741199
Each number is the catalogue of a specific school or college of the University.
Author : J. D. Kalma,M. Sivapalan
Publisher : Advances in Hydrological Proce
Page : 518 pages
File Size : 41,6 Mb
Release : 1995-09-11
Category : Science
ISBN : UCSD:31822020641494
There is a growing need for appropriate models which address the management of land and water resources and ecosystems at large space and time scales. Theories of non-linear hydrological processes must be extrapolated to large-scale, three-dimensional natural systems such as drainage basins, flood plains and wetlands. This book reports on recent progress in research on scale issues in hydrological modelling. It brings together 27 papers from two special issues of the journal Hydrological Processes. The book makes a significant contribution towards developing research strategies for linking model parameterisations across a range of temporal and spatial scales. The papers selected for this book reflect the tremendous advances which have been made in research into scale issues in hydrological modelling during the last ten years.