Data-Based Mechanistic Modelling, Forecasting and Control

Professor Peter C. YoungAfter a student apprenticeship in the aircraft industry, Peter Young was awarded a  B.Tech. degree at Loughborough University and went on to receive an M.Sc. degree for research on Parameter Estimation and Self-Adaptive Control. On the award of a Whitworth Fellowship, he then moved to the University of Cambridge and carried out Ph.D. research on The Differential Equation Error Method of Real-Time Process Identification, with the award of M.A., Ph.D. degrees in 1970. Between 1968 and 1970, he was employed in California as a civilian research scientist with the US Navy. In 1970 he returned to Cambridge University in the UK, where he was appointed a lecturer in the Engineering Department, with an Official Fellowship in Clare Hall. He continued his research on real-time recursive parameter estimation, data-based modelling of dynamic systems and advanced automatic control system design, with applications in various areas of study. Based on this research, he was invited to take up a Professorial Fellowship at the Australian National University, Canberra. From 1975 to 1981, he was the Head of the Systems Section in the Centre for Resource and Environmental Systems, working mainly on the data-based modelling of hydrological and water resource systems, with applications in Canberra, the Peel Inlet of Western Australia and the Northern Territory. In 1981 he returned to the UK to become Professor and Head of the Environmental Science Department at Lancaster University, where he continued his research on various topics concerned with dynamic modelling, non-stationary time-series analysis and automatic control system design. Between 1981 and 2000, he was instrumental in setting up the Institute of Environmental and Biological Sciences; created and led the Centre for Research in Environmental Systems and Statistics; and helped to set up the Lancaster Forecasting Centre. He is now Professor Emeritus in the Lancaster Environmental Centre and a member of the Data Science Institute. He is the author or editor of several books, as well as over 500 publications in various areas of study, including systems and control engineering, non-stationary time-series analysis and forecasting: with applications in environmental science, from hydrology to climate modelling; the modelling and automatic control of glasshouse systems; and macro-economic modelling.

• Introduction: Philosophy and methodology.- Part 1: DBM emulation modelling of large dynamic simulation models.- DBM emulation modelling.- Part 2: The DBM modelling of dynamic systems from real data.- DBM modelling, forecasting and control: Illustrated by its application to globally-averaged climate data.- DBM monitoring, forecasting and modelling: Illustrated by its application to COVID-19 Pandemic Data.- DBM modelling, forecasting and control: Illustrated by its application to USA investment-unemployment data.- Tutorial appendix with hydrological examples.- Basic mathematical and statistical background.