Advances in Streamflow Forecasting: From Traditional to Modern Approaches - Tapa blanda

Acerca de los autores

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.

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.

De la contraportada

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 the background information and overview of streamflow forecasting. Chapters 2-5 describe various parametric stochastic-modelling 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.

Chapters 6-13 explain the advance stage of development and verification of streamflow forecasting models involving artificial intelligence methods. In this section, brief theoretical details and applications of non-parametric methods such as multiple linear regression, Thomas-Fiering model, wavelet analysis, support vector machine (SVM), genetic algorithm (GA), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) are illustrated, and comparisons between parametric methods such as stochastic models and non-parametric or artificial intelligence methods are considered .Finally, Chapters 14-17 include the recent hybrid approaches used to improve the forecast accuracy, and to reduce the uncertainties in streamflow forecasting. The book concludes with a suggested way forward, looking ahead to future needs and challenges in further strengthening streamflow forecasting. 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 non-structural measures), and short-term emergency warning.