Advances in Streamflow Forecasting

From Traditional to Modern Approaches

1st Edition - June 20, 2021
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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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.

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

Priyanka Sharma

Dr. Priyanka Sharma is a postdoctoral student with the biomedical Informatics department at the ICMR. Her work focuses on the field of computational biology involving structural bioinformatics and genomics of bacterial pathogens. She has published widely in the field of clinical bacteriology and is the author of over 30 research papers, reviews and chapters. She has work experience in field of antimicrobial resistance due to enzymatic mutations.

Affiliations and expertise

India Council of Medical Research, New Delhi, Delhi, India

Deepesh Machiwal

Dr. Deepesh Machiwal is Principal Scientist (Soil and Water Conservation Engineering) at ICAR-Central Arid Zone Research Institute (CAZRI), Jodhpur, India. He obtained his Ph.D. from Indian Institute of Technology, Kharagpur in 2009. He has more than 20 years of experience in soil and water conservation engineering and groundwater hydrology. His current research area is modeling groundwater levels in Indian arid region under the changing climate and groundwater demands. Deepesh served from 2005 to 2011 as Assistant Professor in the all India coordinated research project on groundwater utilization at College of Technology and Engineering, Udaipur, India. He has worked as co-principal investigator in three externally-funded research projects funded by ICARDA, ICAR and Government of Rajasthan, India. He has authored one book, edited two books and has contributed 19 book chapters. Deepesh has to his credit 39 papers in international and 19 papers in national journals, 2 technical reports, 4 extension bulletins, 16 popular articles, and 33 papers in conference proceedings. His authored book entitled, Hydrologic Time Series Analysis: Theory and Practice, has been awarded by Outstanding Book Award for 2012-13 from ISAE, New Delhi, India. He has been awarded Commendation Medal Award in 2019 by ISAE, Best Paper Award-2018 by CAZRI, Jodhpur, Achiever Award-2015 by SADHNA, Himachal Pradesh, Distinguished Service Certificate Award for 2012-2013 by ISAE, and IEI Young Engineer Award in 2012 by The Institution of Engineers (India), West Bengal. He is recipient of Foundation Day Award of CAZRI for 2012, 2013 and 2014 and Appreciation Certificate from IEI, Udaipur in 2012. Earlier, he was awarded Junior Research Professional Fellowship by IWMI, Sri Lanka to participate in International Training and Research Program on Groundwater Governance in Asia: Theory and Practice. He has been conferred with Second Best Comprehensive Group Paper Award by IWMI, Sri Lanka in 2007. He was also sponsored by FAO, Rome and UN-Water for participating in two international workshops at China and Indonesia. He is a life member of 8 professional societies and associations. Currently, Deepesh is serving as Advisory Board Member of Ecological Indicators (Elsevier) and has served as Associate Editor for Journal of Agricultural Engineering (ISAE) during 2018-20. He is reviewer of several national and international journals related to soil & water engineering and hydrology.

Affiliations and expertise

Principal Scientist, ICAR-Central Arid Zone Research Institute, Jodhpur, Rajasthan, India

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