+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Advances in Streamflow Forecasting

  • ID: 5230556
  • Book
  • June 2021
  • 450 Pages
  • Elsevier Science and Technology

Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major approaches of streamflow forecasting, including traditional methods such as stochastic time-series modeling, data-driven techniques, and modern techniques of hybrid methods. The book starts by providing background information and an overview of streamflow forecasting. The book concludes with a suggested way forward, looking ahead to future needs and challenges in further strengthening streamflow forecasting. This book is a vital resource for hydrologists optimizing water resource systems to mitigate the impact of destructive natural disasters, such as floods and droughts.

Subsequent chapters describe various parametric stochastic-modeling methods such as auto-regressive moving average (ARMA), auto-regressive integrated moving average (ARIMA), seasonal auto-regressive integrated moving average (SARIMA), de-seasonalized auto-regressive integrated moving average (DARIMA), periodic auto-regressive moving average (PARMA) for simulation and forecasting the streamflow time series. It also includes the comparison of parametric methods to evaluate the best-fitted model for streamflow forecasting, and much more.

  • Provides the most authoritative outlook on stream forecasting for both flood and drought
  • Covers all available methods of streamflow forecasting methods used in the literature and guides the audience to the best method and tool for them
  • Includes multiple case studies (at least one for every method in each chapter) demonstrating the application of each method
Note: Product cover images may vary from those shown

1. An Introduction to Streamflow Forecasting

Section I. Parametric Methods 2. Auto-Regressive Moving Average (ARMA) Model 3. Auto-Regressive Integrated Moving Average (ARIMA) Model 4. Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Deseasonalized Auto-Regressive Integrated Moving Average (DARMA), Periodic Auto-Regressive Moving Average (PARMA), Fractional Auto-Regressive Integrated Moving Average (FARIMA) 5. Comparison of Parametric Models

Section II. Non-Parametric Methods 6. Multiple Linear Regression 7. Thomas-Fiering Model 8. Wavelet Analysis 9. Support Vector Machine (SVM) 10. Genetic Algorithm 11. Artificial Neural Network (ANN) 12. Adaptive Neuro Fuzzy Inference System (ANFIS) 13. Entropy Theory

Section III. Hybrid Approaches 14. A Comparison of Stochastic Models with Artificial Neural Network Technique 15. Application of Hybrid Artificial Neural Network (ANN) model for streamflow Prediction 16. Application of Data driven techniques for streamflow forecasting 17. Adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modelling 18. A Way Forward

Note: Product cover images may vary from those shown
Sharma, Priyanka
Dr. Priyanka Sharma is currently the Assistant Professor at the Faculty of Agriculture Science, Maharishi Arvind University, Jaipur, Rajasthan, India. She completed her B.Tech. (Agril. Engineering) from Chandra Shekhar Azad University of Agriculture & Technology (CSAU&T), Kanpur, India in 2012. She obtained her M.Tech. and Ph.D. from Maharana Pratap University of Agriculture and Technology (MPUAT), Udaipur in 2014 and 2018, respectively. Between March 2016 and June2016 she worked as a Senior Research Fellow in Department of Soil and Water Engineering, College of Technology and Engineering (CTAE), MPUAT, Udaipur, India. From January 2018 toJune 2018, she worked as an Assistant Professor in the School of Agriculture, Lovely Professional University, Phagwara, India. She has published research papers in national refereed journals as well as full-length papers and abstracts in symposiums and conferences. She has also contributed to book chapters. She has received JAE Best Paper Award and Scientist Associate Award. She is also a life member of two national professional societies.
Machiwal, Deepesh
Dr. Deepesh Machiwal is the Principal Scientist at ICAR-Central Arid Zone Research Institute, Jodhpur, Rajasthan. He obtained his Ph.D. from Indian Institute of Technology (IIT) Kharagpur in 2009. He has authored one book, edited two books, and has contributed 18 book chapters. Dr. Machiwal has 35 papers in international and 19 papers in national journals. His book entitled, 'Hydrologic Time Series Analysis: Theory and Practice', is awarded 'Outstanding Book Award'. He has been awarded 'Commendation Medal Award', 'Distinguished Service Certificate Award', 'Best Paper Award-2018', 'Achiever Award-2015', 'Young Engineer Award', and 'Foundation Day Award' of CAZRI, Jodhpur for 2012, 2013 and 2014. Earlier, he was awarded 'Junior Research Professional Fellowship' and 'Second Best Comprehensive Group Paper Award' by IWMI, Sri Lanka. He participated in FAO and UN-Water -sponsored international workshops at China and Indonesia. He is serving as Associate Editor of Journal of Agricultural Engineering (ISAE).
Note: Product cover images may vary from those shown