Applications Of Machine Learning In Hydroclimatology

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Applications of Machine Learning in Hydroclimatology

Author : Roshan Karan Srivastav
Publisher : Springer
ISBN 13 : 9783031644023
Total Pages : 0 pages
Book Rating : 4.6/5 (44 download)

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Book Synopsis Applications of Machine Learning in Hydroclimatology by : Roshan Karan Srivastav

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.

Broadening the Use of Machine Learning in Hydrology

Author : Chaopeng Shen
Publisher : Frontiers Media SA
ISBN 13 : 2889669823
Total Pages : 163 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Broadening the Use of Machine Learning in Hydrology by : Chaopeng Shen

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:

Advanced Hydroinformatics

Author : Gerald A. Corzo Perez
Publisher : John Wiley & Sons
ISBN 13 : 1119639344
Total Pages : 483 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Advanced Hydroinformatics by : Gerald A. Corzo Perez

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.

Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author : Mohammad Ehteram
Publisher : Springer Nature
ISBN 13 : 9811997330
Total Pages : 201 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Application of Machine Learning Models in Agricultural and Meteorological Sciences by : Mohammad Ehteram

Download or read book Application of Machine Learning Models in Agricultural and Meteorological Sciences written by Mohammad Ehteram and published by Springer Nature. This book was released on 2023-03-21 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars.

Understanding Atmospheric Rivers Using Machine Learning

Author : Manish Kumar Goyal
Publisher : Springer Nature
ISBN 13 : 3031634780
Total Pages : 78 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Understanding Atmospheric Rivers Using Machine Learning by : Manish Kumar Goyal

Download or read book Understanding Atmospheric Rivers Using Machine Learning written by Manish Kumar Goyal and published by Springer Nature. This book was released on with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Hydrological Data Driven Modelling

Author : Renji Remesan
Publisher : Springer
ISBN 13 : 3319092359
Total Pages : 261 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Hydrological Data Driven Modelling by : Renji Remesan

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.

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

Author : Arun Lal Srivastav
Publisher : Elsevier
ISBN 13 : 0323997155
Total Pages : 500 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence by : Arun Lal Srivastav

Download or read book Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence written by Arun Lal Srivastav and published by Elsevier. This book was released on 2022-11-11 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world. This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action. Includes case studies on the application of AI and machine learning for monitoring climate change effects and management Features applications of software and algorithms for modeling and forecasting climate change Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

Machine Learning and Data Mining Approaches to Climate Science

Author : Valliappa Lakshmanan
Publisher : Springer
ISBN 13 : 3319172204
Total Pages : 243 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Machine Learning and Data Mining Approaches to Climate Science by : Valliappa Lakshmanan

Download or read book Machine Learning and Data Mining Approaches to Climate Science written by Valliappa Lakshmanan and published by Springer. This book was released on 2015-06-30 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014.

Applications of Information Theory and Machine Learning for Hydrologic Modeling

Author : Andrew R. Bennett
Publisher :
ISBN 13 :
Total Pages : 107 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Applications of Information Theory and Machine Learning for Hydrologic Modeling by : Andrew R. Bennett

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

Handbook of HydroInformatics

Author : Saeid Eslamian
Publisher : Elsevier
ISBN 13 : 0128219505
Total Pages : 420 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Handbook of HydroInformatics by : Saeid Eslamian

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.

Artificial Intelligence Applications in Water Treatment and Water Resource Management

Author : Shikuku, Victor
Publisher : IGI Global
ISBN 13 : 1668467933
Total Pages : 289 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Artificial Intelligence Applications in Water Treatment and Water Resource Management by : Shikuku, Victor

Download or read book Artificial Intelligence Applications in Water Treatment and Water Resource Management written by Shikuku, Victor and published by IGI Global. This book was released on 2023-08-25 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of a plethora of water contaminants as a result of industrialization has introduced complexity to water treatment processes. Such complexity may not be easily resolved using deterministic approaches. Artificial intelligence (AI) has found relevance and applications in almost all sectors and academic disciplines, including water treatment and management. AI provides dependable solutions in the areas of optimization, suspect screening or forensics, classification, regression, and forecasting, all of which are relevant for water research and management. Artificial Intelligence Applications in Water Treatment and Water Resource Management explores the different AI techniques and their applications in wastewater treatment and water management. The book also considers the benefits, challenges, and opportunities for future research. Covering key topics such as water wastage, irrigation, and energy consumption, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Metaheuristic Algorithms and Neural Networks in Hydrology

Author : Kuok King Kuok
Publisher : Cambridge Scholars Publishing
ISBN 13 : 1036408051
Total Pages : 231 pages
Book Rating : 4.0/5 (364 download)

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Book Synopsis Metaheuristic Algorithms and Neural Networks in Hydrology by : Kuok King Kuok

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.

Advanced Information Networking and Applications

Author : Leonard Barolli
Publisher : Springer Nature
ISBN 13 : 303157916X
Total Pages : 510 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Advanced Information Networking and Applications by : Leonard Barolli

Download or read book Advanced Information Networking and Applications written by Leonard Barolli and published by Springer Nature. This book was released on with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Application of Machine Learning for the Prediction of Stable Isotopes of Water Concentrations in Streams and Groundwater

Author : Amirhossein Sahraei
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)

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Book Synopsis Application of Machine Learning for the Prediction of Stable Isotopes of Water Concentrations in Streams and Groundwater by : Amirhossein Sahraei

Download or read book Application of Machine Learning for the Prediction of Stable Isotopes of Water Concentrations in Streams and Groundwater written by Amirhossein Sahraei and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Hydroclimatology of the Great Lakes Region of North America

Author : Julie A. Winkler
Publisher : Frontiers Media SA
ISBN 13 : 2832505457
Total Pages : 243 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Hydroclimatology of the Great Lakes Region of North America by : Julie A. Winkler

Download or read book Hydroclimatology of the Great Lakes Region of North America written by Julie A. Winkler and published by Frontiers Media SA. This book was released on 2022-11-14 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Hydrology and Hydroclimatology

Author : M. Karamouz
Publisher : CRC Press
ISBN 13 : 1466512202
Total Pages : 731 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Hydrology and Hydroclimatology by : M. Karamouz

Download or read book Hydrology and Hydroclimatology written by M. Karamouz and published by CRC Press. This book was released on 2012-11-27 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic approach to understanding and applying the principles of hydrology and hydroclimatology, examining the interactions among different components of the water cycle. It takes a fresh look at the fundamentals and challenges in hydrologic and hydroclimatic systems as well as climate change. The author describes the applic

Machine Learning Techniques for Space Weather

Author : Enrico Camporeale
Publisher : Elsevier
ISBN 13 : 0128117893
Total Pages : 454 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Machine Learning Techniques for Space Weather by : Enrico Camporeale

Download or read book Machine Learning Techniques for Space Weather written by Enrico Camporeale and published by Elsevier. This book was released on 2018-05-31 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms