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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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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
Scientists post graduate students and researchers working in the field of water resources, hydrologists and Water Resources Engineers
Forecasters, Watershed Scientists, Natural Resource/Water Managers, Meteorologists, Civil Engineers, Statisticians
Forecasters, Watershed Scientists, Natural Resource/Water Managers, Meteorologists, Civil Engineers, Statisticians
1. Streamflow Forecasting: Overview and Advances in Data-Driven Techniques
Priyanka Sharma and Deepesh Machiwal
2. Streamflow Forecasting at Large Time Scales Using Statistical Models
Hristos Tyralis, Georgia Papacharalampous and Andreas Langousis
3. Introduction of Multiple/Multivariate Linear and Nonlinear Time Series Models in Forecasting Streamflow Process
Farshad Fathian
4. Concepts and Procedures of Artificial Neural Network Models for Streamflow Forecasting
Arash Malekian and Nastaran Chitsaz
5. Application of Different Artificial Neural Network Models in Streamflow Forecasting
Md. Manjurul Hussain, Sheikh Hefzul Bari, Ishtiak Mahmud, Mohammad Istiyak Hossain Siddiquee
6. Application of Artificial Neural Network Model and Adaptive Neuro-Fuzzy Inference System in Streamflow Forecasting
Mehdi Vafakhah and Saeid Janizadeh
7. Genetic Programming for Streamflow Forecasting: A Concise Review of Univariate Models with a Case Study
Ali Danandeh Mehr and Mir Jafar Sadegh Safari
8. Model Tree Technique for Streamflow Forecasting: A Case Study of a Sub-catchment in Tapi River Basin, India
Priyank J. Sharma, P. L. Patel and V. Jothiprakash
9. Averaging Multi-climate Model Prediction of Streamflow in the Machine Learning Paradigm
Kevin O. Achieng
10. Short-term Flood Forecasting using Artificial Neural Network, Extreme Learning Machines and M5 Tree Models
Mukesh K. Tiwari, Ravinesh C. Deo and Jan F. Adamowski
11. A New Heuristic Method for Monthly Streamflow Forecasting: Outlier-Robust Extreme Learning Machine
Salim Heddam and Özgur Kişi
12. Hybrid Artificial Intelligence Models for Predicting Daily Runoff
Anurag Malik, Anil Kumar, Yazid Tikhamarine, Doudja Souag-Gamane and Özgur Kişi
13. Flood Forecasting and Error Simulation Using Copula and Entropy Methods
Lu Chen and Vijay P. Singh
Priyanka Sharma and Deepesh Machiwal
2. Streamflow Forecasting at Large Time Scales Using Statistical Models
Hristos Tyralis, Georgia Papacharalampous and Andreas Langousis
3. Introduction of Multiple/Multivariate Linear and Nonlinear Time Series Models in Forecasting Streamflow Process
Farshad Fathian
4. Concepts and Procedures of Artificial Neural Network Models for Streamflow Forecasting
Arash Malekian and Nastaran Chitsaz
5. Application of Different Artificial Neural Network Models in Streamflow Forecasting
Md. Manjurul Hussain, Sheikh Hefzul Bari, Ishtiak Mahmud, Mohammad Istiyak Hossain Siddiquee
6. Application of Artificial Neural Network Model and Adaptive Neuro-Fuzzy Inference System in Streamflow Forecasting
Mehdi Vafakhah and Saeid Janizadeh
7. Genetic Programming for Streamflow Forecasting: A Concise Review of Univariate Models with a Case Study
Ali Danandeh Mehr and Mir Jafar Sadegh Safari
8. Model Tree Technique for Streamflow Forecasting: A Case Study of a Sub-catchment in Tapi River Basin, India
Priyank J. Sharma, P. L. Patel and V. Jothiprakash
9. Averaging Multi-climate Model Prediction of Streamflow in the Machine Learning Paradigm
Kevin O. Achieng
10. Short-term Flood Forecasting using Artificial Neural Network, Extreme Learning Machines and M5 Tree Models
Mukesh K. Tiwari, Ravinesh C. Deo and Jan F. Adamowski
11. A New Heuristic Method for Monthly Streamflow Forecasting: Outlier-Robust Extreme Learning Machine
Salim Heddam and Özgur Kişi
12. Hybrid Artificial Intelligence Models for Predicting Daily Runoff
Anurag Malik, Anil Kumar, Yazid Tikhamarine, Doudja Souag-Gamane and Özgur Kişi
13. Flood Forecasting and Error Simulation Using Copula and Entropy Methods
Lu Chen and Vijay P. Singh
- No. of pages: 404
- Language: English
- Edition: 1
- Published: June 20, 2021
- Imprint: Elsevier
- Paperback ISBN: 9780128206737
- eBook ISBN: 9780128209240
PS
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, IndiaDM
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, IndiaRead Advances in Streamflow Forecasting on ScienceDirect