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Identification of parametric models : from experimental data

About: The article was published on 1997-01-01 and is currently open access. It has received 1251 citations till now. The article focuses on the topics: Parametric model & Experimental data.
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30 Aug 2001

1,709 citations


Cites background or result from "Identification of parametric models..."

  • ...This is consistent with the conclusion of an identifiability study (Walter and Pronzato, 1997), which indicates that the system of Figure 6....

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  • ...Consider the discrete-time dynamical system (Walter and Pronzato, 1997) described by y(k + 1) = PIy(k), where y(O) = P2....

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Journal ArticleDOI
TL;DR: General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed.
Abstract: In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.

1,207 citations

Journal ArticleDOI
TL;DR: In this article, the authors outline ten basic steps of good, disciplined model practice, including identifying clearly the clients and objectives of the modelling exercise, documenting the nature (quantity, quality, limitations) of the data used to construct and test the model, providing a strong rationale for the choice of model family and features, justifying the techniques used to calibrate the model; serious analysis, testing and discussion of model performance; and making a resultant statement of model assumptions, utility, accuracy, limitations, and scope for improvement.
Abstract: Models are increasingly being relied upon to inform and support natural resource management. They are incorporating an ever broader range of disciplines and now often confront people without strong quantitative or model-building backgrounds. These trends imply a need for wider awareness of what constitutes good model-development practice, including reporting of models to users and sceptical review of models by users. To this end the paper outlines ten basic steps of good, disciplined model practice. The aim is to develop purposeful, credible models from data and prior knowledge, in consort with end-users, with every stage open to critical review and revision. Best practice entails identifying clearly the clients and objectives of the modelling exercise; documenting the nature (quantity, quality, limitations) of the data used to construct and test the model; providing a strong rationale for the choice of model family and features (encompassing review of alternative approaches); justifying the techniques used to calibrate the model; serious analysis, testing and discussion of model performance; and making a resultant statement of model assumptions, utility, accuracy, limitations, and scope for improvement. In natural resource management applications, these steps will be a learning process, even a partnership, between model developers, clients and other interested parties.

937 citations

Journal ArticleDOI
TL;DR: An overview and critical analysis of the state of the art in this sector are proposed and the main contributions to model-based experiment design procedures in terms of novel criteria, mathematical formulations and numerical implementations are highlighted.

650 citations

Journal ArticleDOI
TL;DR: The major methods for measuring respiration rates, along with their advantages and limitations are discussed in this paper, stressing the importance of temperature, O2 and CO2 concentrations, and storage time.

635 citations


Cites background from "Identification of parametric models..."

  • ...The dif- ferent model equations would not be statistically distinguishable from each other due to experimental error (Walter & Pronzato, 1997)....

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