Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems

Peter C. Young (1) , Hugues Garnier (2)

1 CRES - Centre for Research on Environmental Systems and Statistics
2 CRAN - Centre de Recherche en Automatique de Nancy

Initially, the paper provides an introduction to the main aspects of existing time-domain methods for identifying linear continuous-time models from discrete-time data and shows how one of these methods has been applied to the identification and estimation of a model for the transportation and dispersion of a pollutant in a river. It then introduces a widely applicable class of new, nonlinear, State Dependent Parameter (SDP) models. Finally, the paper describes how this SDP approach has been used to identify, estimate and control a nonlinear differential equation model of global carbon cycle dynamics and global warming.

Mots clés: Continuous-time; Stochastic; Linear; Instrumental variable; Optimal estimation; State dependent parameter; Nonlinear; Environmental

Domaines: Automatique / Robotique

Peter C. Young, Hugues Garnier. Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems. Environmental Modelling and Software, 2006, 21 (8), pp.1055-1072. ⟨10.1016/j.envsoft.2005.05.007⟩. ⟨hal-00119818⟩