A methodological framework for the validation of predictive simulations

Fraedrich, Douglas S.; Goldberg, Allen J.

doi:10.1016/s0377-2217(99)00117-4

Cited by 37 publications

(19 citation statements)

References 13 publications

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“…Pappenberger et al [46,47] and Hall et al [21] present cases that try to achieve such an understanding for flood inundation models with the help of sensitivity analysis (SA). The study of Pappenberger et al [47] highlights the need for an iterative modelling process based on sensitivity analysis to refine, develop and understand flood inundation models (see also [15,18,30,31]). Hall et al [21] analyse the sensitivity of the Manning channel roughness parameter changes along the river and highlight areas in which improved sampling or modelling strategies could improve flood inundation predictions.…”

Section: Introductionmentioning

confidence: 99%

Multi-method global sensitivity analysis of flood inundation models

Pappenberger

Beven

Ratto

et al. 2008

Advances in Water Resources

243

201

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Section: Introductionmentioning

confidence: 99%

Multi-method global sensitivity analysis of flood inundation models

Pappenberger

Beven

Ratto

et al. 2008

Advances in Water Resources

243

201

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“…Lessons learned from these many previous trials, as well as methodological procedures developed by NRL [5], were used in designing this trial.…”

Section: Validation Analysis Methodologymentioning

confidence: 99%

ShipIR model validation using NATO SIMVEX experiment results

et al. 2003

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“…Whilst these are not generally well developed for DDMs, conceptual and physically based modellers have made extensive use of relative parameter sensitivity analysis (Hamby, 1994) to elucidate the mechanistic behaviour of their models (Howes and Anderson, 1988) and strengthen their validation (e.g. Kleijnen, 1995;Kleijnen and Sargent, 2000;Fraedrich and Goldberg, 2000;Smith et al, 2008;Mishra, 2009). Critically, it has been shown to be an important means by which model validation can be extended beyond fit, to include deeper insights into the legitimacy of a model's mechanistic behaviours (e.g.…”

Section: Enabling the Ddmmf For Ann Models: Revealing Mechanistic Behmentioning

confidence: 99%

Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

Mount

Dawson

Abrahart

2013

Hydrol. Earth Syst. Sci.

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Abstract.In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model's internal modelling mechanism as a core element in the modelling process. The framework's value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model's mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior.

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A methodological framework for the validation of predictive simulations

Cited by 37 publications

References 13 publications

Multi-method global sensitivity analysis of flood inundation models

Multi-method global sensitivity analysis of flood inundation models

ShipIR model validation using NATO SIMVEX experiment results

Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

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