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  • Mindham et al 2018 accepted (1)

    Rights statement: 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

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

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>06/2018
<mark>Journal</mark>Environmental Modelling and Software
Volume104
Number of pages13
Pages (from-to)81-93
Publication statusPublished
Early online date24/03/18
Original languageEnglish

Abstract

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.

Bibliographic note

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