1. Streamflow Forecasting: Overview and Advances in Data-Driven Techniques 2. Streamflow Forecasting at Large Time Scales Using Statistical Models 3. Introduction of Multiple/Multivariate Linear and Nonlinear Time Series Models in Forecasting Streamflow Process 4. Concepts and Procedures of Artificial Neural Network Models for Streamflow Forecasting 5. Application of Different Artificial Neural Network Models in Streamflow Forecasting 6. Application of Artificial Neural Network Model and Adaptive Neuro-Fuzzy Inference System in Streamflow Forecasting 7. Genetic Programming for Streamflow Forecasting: A Concise Review of Univariate Models with a Case Study 8. Model Tree Technique for Streamflow Forecasting: A Case Study of a Sub-catchment in Tapi River Basin, India 9. Averaging Multi-climate Model Prediction of Streamflow in the Machine Learning Paradigm 10. Short-term Flood Forecasting using Artificial Neural Network, Extreme Learning Machines and M5 Tree Models 11. A New Heuristic Method for Monthly Streamflow Forecasting: Outlier-Robust Extreme Learning Machine 12. Hybrid Artificial Intelligence Models for Predicting Daily Runoff 13. Flood Forecasting and Error Simulation Using Copula and Entropy Methods
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.