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

P. C. Young; H. Garnier

doi:10.1016/j.envsoft.2005.05.007

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

P. C. Young^*, H. Garnier

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

110 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1055-1072
Number of pages	18
Journal	Environmental Modelling and Software
Volume	21
Issue number	8
DOIs	https://doi.org/10.1016/j.envsoft.2005.05.007
Publication status	Published - Aug 2006

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10.1016/j.envsoft.2005.05.007

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

abstract = "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.",

keywords = "Continuous-time, Environmental, Instrumental variable, Linear, Nonlinear, Optimal estimation, State dependent parameter, Stochastic",

author = "Young, \{P. C.\} and H. Garnier",

year = "2006",

month = aug,

doi = "10.1016/j.envsoft.2005.05.007",

language = "English",

volume = "21",

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journal = "Environmental Modelling and Software",

issn = "1364-8152",

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T1 - Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems

AU - Young, P. C.

AU - Garnier, H.

PY - 2006/8

Y1 - 2006/8

N2 - 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.

AB - 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.

KW - Continuous-time

KW - Environmental

KW - Instrumental variable

KW - Linear

KW - Nonlinear

KW - Optimal estimation

KW - State dependent parameter

KW - Stochastic

UR - https://www.scopus.com/pages/publications/33646505058

U2 - 10.1016/j.envsoft.2005.05.007

DO - 10.1016/j.envsoft.2005.05.007

M3 - Article

SN - 1364-8152

VL - 21

SP - 1055

EP - 1072

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

IS - 8

ER -

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

Abstract

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