
Advances in Streamflow Forecasting
From Traditional to Modern Approaches
- 1st Edition - June 20, 2021
- Imprint: Elsevier
- Editors: Priyanka Sharma, Deepesh Machiwal
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 6 7 3 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 9 2 4 - 0
Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of… Read more

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Request a sales quoteAdvances 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
Forecasters, Watershed Scientists, Natural Resource/Water Managers, Meteorologists, Civil Engineers, Statisticians
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- About the editors
- Foreword
- Preface
- Acknowledgment
- Chapter 1. Streamflow forecasting: overview of advances in data-driven techniques
- 1.1. Introduction
- 1.2. Measurement of streamflow and its forecasting
- 1.3. Classification of techniques/models used for streamflow forecasting
- 1.4. Growth of data-driven methods and their applications in streamflow forecasting
- 1.5. Comparison of different data-driven techniques
- 1.6. Current trends in streamflow forecasting
- 1.7. Key challenges in forecasting of streamflows
- 1.8. Concluding remarks
- Chapter 2. Streamflow forecasting at large time scales using statistical models
- 2.1. Introduction
- 2.2. Overview of statistical models used in forecasting
- 2.3. Theory
- 2.4. Large-scale applications at two time scales
- 2.5. Conclusions
- Conflicts of interest
- Chapter 3. Introduction of multiple/multivariate linear and nonlinear time series models in forecasting streamflow process
- 3.1. Introduction
- 3.2. Methodology
- 3.3. Application of VAR/VARX approach
- 3.4. Application of MGARCH approach
- 3.5. Comparative evaluation of models’ performances
- 3.6. Conclusions
- Chapter 4. Concepts, procedures, and applications of artificial neural network models in streamflow forecasting
- 4.1. Introduction
- 4.2. Procedure for development of artificial neural network models
- 4.3. Types of artificial neural networks
- 4.4. An overview of application of artificial neural network modeling in streamflow forecasting
- Chapter 5. Application of different artificial neural network for streamflow forecasting
- 5.1. Introduction
- 5.2. Development of neural network technique
- 5.3. Artificial neural network in streamflow forecasting
- 5.4. Application of ANN: a case study of the Ganges River
- 5.5. ANN application software and programming language
- 5.6. Conclusions
- 5.7. Supplementary information
- Chapter 6. Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting
- 6.1. Introduction
- 6.2. Theoretical description of models
- 6.3. Application of ANN and ANFIS for prediction of peak discharge and runoff: a case study
- 6.4. Results and discussion
- 6.5. Conclusions
- Chapter 7. Genetic programming for streamflow forecasting: a concise review of univariate models with a case study
- 7.1. Introduction
- 7.2. Overview of genetic programming and its variants
- 7.3. A brief review of the recent studies
- 7.4. A case study
- 7.5. Results and discussion
- 7.6. Conclusions
- Chapter 8. Model tree technique for streamflow forecasting: a case study in sub-catchment of Tapi River Basin, India
- 8.1. Introduction
- 8.2. Model tree
- 8.3. Model tree applications in streamflow forecasting
- 8.4. Application of model tree in streamflow forecasting: a case study
- 8.5. Results and analysis
- 8.6. Summary and conclusions
- Chapter 9. Averaging multiclimate model prediction of streamflow in the machine learning paradigm
- 9.1. Introduction
- 9.2. Salient review on ANN and SVR modeling for streamflow forecasting
- 9.3. Averaging streamflow predicted from multiclimate models in the neural network framework
- 9.4. Averaging streamflow predicted by multiclimate models in the framework of support vector regression
- 9.5. Machine learning–averaged streamflow from multiple climate models: two case studies
- 9.6. Conclusions
- Chapter 10. Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree
- 10.1. Introduction
- 10.2. Theoretical background
- 10.3. Application of ANN, ELM, and M5 model tree techniques in hourly flood forecasting: a case study
- 10.4. Results and discussion
- 10.5. Conclusions
- Chapter 11. A new heuristic model for monthly streamflow forecasting: outlier-robust extreme learning machine
- 11.1. Introduction
- 11.2. Overview of extreme learning machine and multiple linear regression
- 11.3. A case study of forecasting streamflows using extreme machine learning models
- 11.4. Applications and results
- 11.5. Conclusions
- Chapter 12. Hybrid artificial intelligence models for predicting daily runoff
- 12.1. Introduction
- 12.2. Theoretical background of MLP and SVR models
- 12.3. Application of hybrid MLP and SVR models in runoff prediction: a case study
- 12.4. Results and discussion
- 12.5. Conclusions
- Chapter 13. Flood forecasting and error simulation using copula entropy method
- 13.1. Introduction
- 13.2. Background
- 13.3. Determination of ANN model inputs based on copula entropy
- 13.4. Flood forecast uncertainties
- 13.5. Flood forecast uncertainty simulation
- 13.6. Conclusions
- Appendix 1. Books and book chapters on data-driven approaches
- Appendix 2. List of peer-reviewed journals on data-driven approaches
- Appendix 3 Data and software
- Index
- Edition: 1
- Published: June 20, 2021
- Imprint: Elsevier
- No. of pages: 404
- Language: English
- Paperback ISBN: 9780128206737
- eBook ISBN: 9780128209240
PS
Priyanka Sharma
DM