Cover for Advances in Streamflow Forecasting

Advances in Streamflow Forecasting

From Traditional to Modern Approaches

Book2021

Edited by:

Priyanka Sharma and Deepesh Machiwal

Advances in Streamflow Forecasting

From Traditional to Modern Approaches

Book2021

 

Cover for Advances in Streamflow Forecasting

Edited by:

Priyanka Sharma and Deepesh Machiwal

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Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Streamflow forecasting: overview of advances in data-driven techniques

    Priyanka Sharma and Deepesh Machiwal

    Pages 1-50

  3. Book chapterAbstract only

    Chapter 2 - Streamflow forecasting at large time scales using statistical models

    Hristos Tyralis, Georgia Papacharalampous and Andreas Langousis

    Pages 51-86

  4. Book chapterAbstract only

    Chapter 3 - Introduction of multiple/multivariate linear and nonlinear time series models in forecasting streamflow process

    Farshad Fathian

    Pages 87-113

  5. Book chapterAbstract only

    Chapter 4 - Concepts, procedures, and applications of artificial neural network models in streamflow forecasting

    Arash Malekian and Nastaran Chitsaz

    Pages 115-147

  6. Book chapterAbstract only

    Chapter 5 - Application of different artificial neural network for streamflow forecasting

    Md Manjurul Hussain, Sheikh Hefzul Bari, ... Mohammad Istiyak Hossain Siddiquee

    Pages 149-170

  7. Book chapterAbstract only

    Chapter 6 - Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting

    Mehdi Vafakhah and Saeid Janizadeh

    Pages 171-191

  8. Book chapterAbstract only

    Chapter 7 - Genetic programming for streamflow forecasting: a concise review of univariate models with a case study

    Ali Danandeh Mehr and Mir Jafar Sadegh Safari

    Pages 193-214

  9. Book chapterAbstract only

    Chapter 8 - Model tree technique for streamflow forecasting: a case study in sub-catchment of Tapi River Basin, India

    Priyank J. Sharma, P.L. Patel and V. Jothiprakash

    Pages 215-237

  10. Book chapterAbstract only

    Chapter 9 - Averaging multiclimate model prediction of streamflow in the machine learning paradigm

    Kevin O. Achieng

    Pages 239-262

  11. Book chapterAbstract only

    Chapter 10 - Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree

    Mukesh K. Tiwari, Ravinesh C. Deo and Jan F. Adamowski

    Pages 263-279

  12. Book chapterAbstract only

    Chapter 11 - A new heuristic model for monthly streamflow forecasting: outlier-robust extreme learning machine

    Salim Heddam and Özgur Kişi

    Pages 281-303

  13. Book chapterAbstract only

    Chapter 12 - Hybrid artificial intelligence models for predicting daily runoff

    Anurag Malik, Anil Kumar, ... Özgur Kişi

    Pages 305-329

  14. Book chapterAbstract only

    Chapter 13 - Flood forecasting and error simulation using copula entropy method

    Lu Chen and Vijay P. Singh

    Pages 331-368

  15. Book chapterNo access

    Appendix 1 - Books and book chapters on data-driven approaches

    Pages 369-370

  16. Book chapterNo access

    Appendix 2 - List of peer-reviewed journals on data-driven approaches

    Pages 371-372

  17. Book chapterNo access

    Appendix 3 Data and software

    Page 373

  18. Book chapterNo access

    Index

    Pages 375-381

About the 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.

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.

Key Features

  • 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
  • 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

Details

ISBN

978-0-12-820673-7

Language

English

Published

2021

Copyright

Copyright © 2021 Elsevier Inc. All rights reserved.

Imprint

Elsevier

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Editors

Priyanka Sharma

Deepesh Machiwal