Data-Based Mechanistic Modelling, Forecasting and Control: The Art and Craft

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 dyna