Data-Based Mechanistic Modelling, Forecasting and Control
Description
This book builds on the methods introduced in the author's previous Springer book Recursive Estimation and Time-Series Analysis to deliver a powerful and practical framework for Data-Based Mechanistic (DBM) modelling directly from time-series data. DBM modelling produces interpretable continuous-time transfer function models, allowing the underlying linear or nonlinear differential equations to be understood in clear physical terms closely tied to the real dynamics behind the data. All modelling tools are freely available in dedicated toolboxes for MATLAB, including advanced modules for forecasting and control based on DBM models. The four application chapters provide an in-depth perspective on DBM modelling, while a separate tutorial appendix offers a guided, step-by-step introduction to the complete DBM workflow using accessible hydrological examples. These methodological foundations are then illustrated by their application in three major areas of topical importance: global climate dynamics, the COVID-19 epidemic, and investment-unemployment interactions in the USA. This book also shows how large-scale simulation models can be distilled into compact, transparent DBM representations, and how these small 'emulation' models can deliver physical insight, reliable forecasting, and effective control (management) strategies. Blending rigorous estimation theory, real-world relevance, and fully reproducible tools, this book offers a unique bridge between advanced data-based modelling and today's most pressing dynamical problems.
More details
Person
Professor Peter C. Young
After 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.
Content
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.