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Author : Chaopeng Shen
Publisher : Frontiers Media SA
ISBN 13 : 2889669823
Total Pages : 163 pages
Book Rating : 4.8/5 (896 download)
Download or read book Broadening the Use of Machine Learning in Hydrology written by Chaopeng Shen and published by Frontiers Media SA. This book was released on 2021-07-08 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Saeid Eslamian
Publisher : Elsevier
ISBN 13 : 012821970X
Total Pages : 484 pages
Book Rating : 4.1/5 (282 download)
Download or read book Handbook of HydroInformatics written by Saeid Eslamian and published by Elsevier. This book was released on 2022-11-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : M. Z. Naser
Publisher : John Wiley & Sons
ISBN 13 : 1119897610
Total Pages : 610 pages
Book Rating : 4.1/5 (198 download)
Download or read book Machine Learning for Civil and Environmental Engineers written by M. Z. Naser and published by John Wiley & Sons. This book was released on 2023-07-17 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Philippe Gourbesville
Publisher : Springer Nature
ISBN 13 : 9819740762
Total Pages : 512 pages
Book Rating : 4.8/5 (197 download)
Download or read book Advances in Hydroinformatics—SimHydro 2023 Volume 2 written by Philippe Gourbesville and published by Springer Nature. This book was released on with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Adam E. Ahmed
Publisher : Springer Nature
ISBN 13 : 3031467043
Total Pages : 500 pages
Book Rating : 4.0/5 (314 download)
Download or read book Food and Nutrition Security in the Kingdom of Saudi Arabia, Vol. 2 written by Adam E. Ahmed and published by Springer Nature. This book was released on 2024-01-31 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Publisher : John Wiley & Sons
ISBN 13 : 1119639344
Total Pages : 483 pages
Book Rating : 4.1/5 (196 download)
Download or read book Advanced Hydroinformatics written by Gerald A. Corzo Perez and published by John Wiley & Sons. This book was released on 2023-12-12 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Taesam Lee
Publisher : Springer Nature
ISBN 13 : 3030647773
Total Pages : 215 pages
Book Rating : 4.0/5 (36 download)
Download or read book Deep Learning for Hydrometeorology and Environmental Science written by Taesam Lee and published by Springer Nature. This book was released on 2021-01-27 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Roshan Karan Srivastav
Publisher : Springer
ISBN 13 : 9783031644023
Total Pages : 0 pages
Book Rating : 4.6/5 (44 download)
Download or read book Applications of Machine Learning in Hydroclimatology written by Roshan Karan Srivastav and published by Springer. This book was released on 2024-10-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Renji Remesan
Publisher : Springer
ISBN 13 : 3319092359
Total Pages : 261 pages
Book Rating : 4.3/5 (19 download)
Download or read book Hydrological Data Driven Modelling written by Renji Remesan and published by Springer. This book was released on 2014-11-03 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Saeid Eslamian
Publisher : Elsevier
ISBN 13 : 0128219505
Total Pages : 420 pages
Book Rating : 4.1/5 (282 download)
Download or read book Handbook of HydroInformatics written by Saeid Eslamian and published by Elsevier. This book was released on 2022-12-06 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This is an interdisciplinary book, 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, Chemical Engineering. Key insights from 24 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. Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees.
Author : Delphis F. Levia
Publisher : Springer Nature
ISBN 13 : 3030260860
Total Pages : 629 pages
Book Rating : 4.0/5 (32 download)
Download or read book Forest-Water Interactions written by Delphis F. Levia and published by Springer Nature. This book was released on 2020-02-05 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: The United Nations has declared 2018-2028 as the International Decade for Action on Water for Sustainable Development. This is a timely designation. In an increasingly thirsty world, the subject of forest-water interactions is of critical importance to the achievement of sustainability goals. The central underlying tenet of this book is that the hydrologic community can conduct better science and make a more meaningful impact to the world’s water crisis if scientists are: (1) better equipped to utilize new methods and harness big data from either or both high-frequency sensors and long-term research watersheds; and (2) aware of new developments in our process-based understanding of the hydrological cycle in both natural and urban settings. Accordingly, this forward-looking book delves into forest-water interactions from multiple methodological, statistical, and process-based perspectives (with some chapters featuring data sets and open-source R code), concluding with a chapter on future forest hydrology under global change. Thus, this book describes the opportunities of convergence in high-frequency sensing, big data, and open source software to catalyze more comprehensive understanding of forest-water interactions. The book will be of interest to researchers, graduate students, and advanced undergraduates in an array of disciplines, including hydrology, forestry, ecology, botany, and environmental engineering.
Author : Yadav, Sudesh
Publisher : IGI Global
ISBN 13 :
Total Pages : 428 pages
Book Rating : 4.3/5 (693 download)
Download or read book Novel AI Applications for Advancing Earth Sciences written by Yadav, Sudesh and published by IGI Global. This book was released on 2023-12-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Earth Sciences industry faces a new challenge - the need for accurate, efficient, and reliable methods to monitor and predict geological phenomena and environmental changes. As climate change, earthquakes, and other natural disasters become more frequent and severe, the necessity for advanced tools and techniques is paramount. Traditional methods often fall short in providing the precision and speed required to address these critical issues. Geologists and earth scientists who are grappling with the urgent problem of utilizing artificial intelligence (AI) to revolutionize their field, will find the solution within the pages of Novel AI Applications for Advancing Earth Sciences. This book offers the research community concepts expanding upon the fusion of AI technology with earth sciences. By leveraging advanced AI tools, such as convolutional neural networks, support vector machines, artificial neural networks, and the potential of remote sensing satellites, this book transforms the identification of geological features, geological mapping, soil classification, and gas detection. Scientists can now predict earthquakes and assess the probability of climate change with unprecedented accuracy. Additionally, the book explains how the optimization of algorithms for specific tasks substantially reduces the time complexity of earth observations, leading to an unprecedented leap in accuracy and efficiency.
Author : Kuok King Kuok
Publisher : Cambridge Scholars Publishing
ISBN 13 : 1036408051
Total Pages : 231 pages
Book Rating : 4.0/5 (364 download)
Download or read book Metaheuristic Algorithms and Neural Networks in Hydrology written by Kuok King Kuok and published by Cambridge Scholars Publishing. This book was released on 2024-08-28 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the latest research and developments related to the application of nature-inspired metaheuristic algorithms coupled with artificial neural networks (ANNs) in hydrology. The book covers the theoretical foundations, models and methods, structure, frameworks and analysis of applying novel ANNs in hydrology. It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.
Author : Andrew R. Bennett
Publisher :
ISBN 13 :
Total Pages : 107 pages
Book Rating : 4.:/5 (126 download)
Download or read book Applications of Information Theory and Machine Learning for Hydrologic Modeling written by Andrew R. Bennett and published by . This book was released on 2021 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: An explosion of new data sources, expansion of computing resources, and theoretical advancesin data science have spurred the rapid adaptation of data-driven methods in earth system science, including hydrology. In this dissertation I will describe three applications of data-driven methods with applications to hydrologic modeling. In chapter 2 I present a framework for hydrologic model intercomparison which examines process interactions within a process-based hydrologic model (PBHM). I show that taking a more holistic approach can shed light into the functioning of these complex models. In chapter 3 I couple machine learned representations of turbulent heat fluxes into a PBHM, and show that neural networks can provide better predictions and transferability than the process-based equations that are used in PBHMs. Building on this, in chapter 4 I use explainable AI (XAI) methods to examine what the neural network has learned. I find that the neural network is able to learn physically plausible relationships and can identify how to partition between latent and sensible heat fluxes based only on short-term temporal data. I also show how we can use XAI to examine what neural networks have learned between sites.This method can uncover that certain sites can be used as predictors for many other sites, as well as that site specific traits such as vegetation type play a large role in the neural network’s ability to generalize to sites it was not trained on. Finally, based on the findings of these three applications I discuss in Chapter 5 how data-driven techniques in general can contribute to improved hydrologic understanding
Author : Rahul Datta
Publisher : BoD – Books on Demand
ISBN 13 : 1837681104
Total Pages : 126 pages
Book Rating : 4.8/5 (376 download)
Download or read book New Insights in Soil-Water Relationship written by Rahul Datta and published by BoD – Books on Demand. This book was released on 2024-07-24 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Humberto Blanco
Publisher : CSIRO PUBLISHING
ISBN 13 : 1486313787
Total Pages : 271 pages
Book Rating : 4.4/5 (863 download)
Download or read book Soil Hydrology in a Changing Climate written by Humberto Blanco and published by CSIRO PUBLISHING. This book was released on 2022-12 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: A changing climate is causing challenges for soil and water management in many parts of the world. Current soil management practices need to be redesigned to effectively address present and future fluctuating climates. Soil Hydrology in a Changing Climate explores how soil management practices impact soil hydrological characteristics, and how we can improve our understanding of soil and water management under changing conditions. Soil hydrology includes water infiltration and soil water storage, which are critical for agricultural plant and animal production. With our future climate predicted to include hotter, drier conditions, increases in evapotranspiration as well as fewer, more intense storms, improved soil management and soil hydrology are critical to ensuring our agriculture production can meet human demand. This comprehensive book is a valuable resource for land managers, soil conservationists, researchers and others who wish to understand how different management practices affect soil and water dynamics and how these practices can be adjusted to enhance agricultural sustainability and environmental quality.
Author : John T. Van Stan II
Publisher : Springer Nature
ISBN 13 : 3031497686
Total Pages : 169 pages
Book Rating : 4.0/5 (314 download)
Download or read book Hydrology and Its Discontents written by John T. Van Stan II and published by Springer Nature. This book was released on with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:
