TY - JOUR

T1 - Extended State Dependent Parameter modelling with a Data-Based Mechanistic approach to nonlinear model structure identification

AU - Mindham, David A.

AU - Tych, Wlodzimierz

AU - Chappell, Nick A.

N1 - This is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software, 104, 2018 DOI: 10.1016/j.envsoft.2018.02.015

PY - 2018/6

Y1 - 2018/6

N2 - Abstract A unified approach to Multiple and single State Dependent Parameter modelling, termed Extended State Dependent Parameters (ESDP) modelling, of nonlinear dynamic systems described by time-varying dynamic models applied to ARX or transfer-function model structures. Crucially, the approach proposes an effective model structure identification method using a novel Information Criterion (IC) taking into account model complexity in terms of the number of states involved. In ESDP, model structure involves not only the model orders, but also selection of the states driving the parameters, which effectively prevents the use of most current IC. This leads to a powerful methodology for investigating nonlinear systems building on the Data-Based Mechanistic (DBM) philosophy of Young and expanding the applications of the existing DBM methods. The methodologies presented are tested and demonstrated on both simulated data and on high frequency hydrological observations, showing how structure identification leads to discovery of dynamic relationships between system variables.

AB - Abstract A unified approach to Multiple and single State Dependent Parameter modelling, termed Extended State Dependent Parameters (ESDP) modelling, of nonlinear dynamic systems described by time-varying dynamic models applied to ARX or transfer-function model structures. Crucially, the approach proposes an effective model structure identification method using a novel Information Criterion (IC) taking into account model complexity in terms of the number of states involved. In ESDP, model structure involves not only the model orders, but also selection of the states driving the parameters, which effectively prevents the use of most current IC. This leads to a powerful methodology for investigating nonlinear systems building on the Data-Based Mechanistic (DBM) philosophy of Young and expanding the applications of the existing DBM methods. The methodologies presented are tested and demonstrated on both simulated data and on high frequency hydrological observations, showing how structure identification leads to discovery of dynamic relationships between system variables.

KW - Nonlinear dynamic systems

KW - Model identification

KW - Data based mechanistic modelling

KW - State-dependent parameters

KW - Hydrological modelling

KW - Information Criterion

U2 - 10.1016/j.envsoft.2018.02.015

DO - 10.1016/j.envsoft.2018.02.015

M3 - Journal article

VL - 104

SP - 81

EP - 93

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -