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Author: Priyanka Sharma
Publisher: Elsevier
ISBN: 0128209240
Category : Science
Languages : en
Pages : 406
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Book Description
Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions. - Contributions from renowned researchers/experts of the subject from all over the world to provide the most authoritative outlook on streamflow forecasting - Provides an excellent overview and advances made in streamflow forecasting over the past more than five decades and covers both traditional and modern data-driven approaches in streamflow forecasting - Includes case studies along with detailed flowcharts demonstrating a systematic application of different data-driven models in streamflow forecasting, which helps understand the step-by-step procedures
Author: Priyanka Sharma
Publisher: Elsevier
ISBN: 0128209240
Category : Science
Languages : en
Pages : 406
Get Book Here
Book Description
Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions. - Contributions from renowned researchers/experts of the subject from all over the world to provide the most authoritative outlook on streamflow forecasting - Provides an excellent overview and advances made in streamflow forecasting over the past more than five decades and covers both traditional and modern data-driven approaches in streamflow forecasting - Includes case studies along with detailed flowcharts demonstrating a systematic application of different data-driven models in streamflow forecasting, which helps understand the step-by-step procedures
Author: Bellie Sivakumar
Publisher: World Scientific
ISBN: 9814464759
Category : Science
Languages : en
Pages : 542
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Book Description
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309060761
Category : Science
Languages : en
Pages : 149
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Book Description
Hydrologic science, an important, interdisciplinary science dealing with the occurrence, distribution, and properties of water on Earth, is key to understanding and resolving many contemporary, large-scale environmental issues. The Water Science and Technology Board used the opportunity of its 1997 Abel Wolman Distinguished Lecture to assess the vitality of the hydrologic sciences by the hydrologic community. The format included focus by lecturer Thomas Dunne on the intellectual vitality of the hydrologic sciences, followed by a symposium featuring several invited papers and discussions. Hydrologic Sciences is a compilation of the Wolman Lecture and the papers, preceded by a summarizing overview. The volume stresses a number of needs for furtherance of hydrologic science, including development of a coherent body of transferable theory and an intellectual center for the science, communication across multiple geo- and environmental science disciplines, appropriate measurements and observations, and provision of central guidance for the field.
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3039368044
Category : Technology & Engineering
Languages : en
Pages : 274
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Book Description
The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: Kevin Sene
Publisher: Springer Science & Business Media
ISBN: 904813403X
Category : Science
Languages : en
Pages : 356
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Book Description
This book describes recent developments in hydrometeorological forecasting techniques for a range of timescales, from short term to seasonal and longer terms. It conveniently brings together both meteorological and hydrological aspects in a single volume.
Author: Philippe Gourbesville
Publisher: Springer Science & Business Media
ISBN: 9814451428
Category : Science
Languages : en
Pages : 545
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Book Description
The book is a collection of extended papers which have been selected for presentation during the SIMHYDRO 2012 conference held in Sophia Antipolis in September 2012. The papers present the state of the art numerical simulation in domains such as (1) New trends in modelling for marine, river & urban hydraulics; (2) Stakeholders & practitioners of simulation; (3) 3D CFD & applications. All papers have been peer reviewed and by scientific committee members with report about quality, content and originality. The target audience for this book includes scientists, engineers and practitioners involved in the field of numerical modelling in the water sector: flood management, natural resources preservation, hydraulic machineries, and innovation in numerical methods, 3D developments and applications.
Author: Hong-sang Jung
Publisher: World Scientific
ISBN: 9813235675
Category : Science
Languages : en
Pages : 231
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Book Description
Since 1980, the number of climate-related disasters has been greatly increased globally. Scientific consensus based on the IPCC fifth report suggested that global warming would bring more intense and frequent extreme climate events. These climate-related disasters hinder the achievement of sustainable economic growth and prosperity by disrupting supply chains, impeding production, destroying infrastructure, and necessitating high-cost rebuilding and recovery. To mitigate the climate extreme risks and possible losses, it is essential to maximize the utilization of scientific outputs and to share best practices in disaster risk management. Aligned with such purposes, Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) hosts the APEC Climate Symposium (APCS) every year. APCS focused on drought prediction and management in 2013, climate extremes and hydrological disaster in 2014, and efficient use of climate information for disaster risk management in 2015.This book aims to compile some of the important results from the latest research in climate extreme prediction and services and its application studies with a focus on climate extremes such as typhoons, droughts, and floods based on the APCS presentations during 2013-2015.
Author: Jose D. Salas
Publisher: Water Resources Publication
ISBN: 9780918334374
Category : Science
Languages : en
Pages : 502
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Book Description
Author: Wen Wang
Publisher: IOS Press
ISBN: 9781586036218
Category : Computers
Languages : en
Pages : 220
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Book Description
Streamflow forecasting is of great importance to water resources management and flood defense. On the other hand, a better understanding of the streamflow process is fundamental for improving the skill of streamflow forecasting. The methods for forecasting streamflows may fall into two general classes: process-driven methods and data-driven methods. Equivalently, methods for understanding streamflow processes may also be broken into two categories: physically-based methods and mathematically-based methods. This thesis focuses on using mathematically-based methods to analyze stochasticity and nonlinearity of streamflow processes based on univariate historic streamflow records, and presents data-driven models that are also mainly based on univariate streamflow time series. Six streamflow processes of five rivers in different geological regions are investigated for stochasticity and nonlinearity at several characteristic timescales.
