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You searched for subject:(effective transport difference). Showing records 1 – 2 of 2 total matches.

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1. Mol, M. (author). The Effective Transport Difference: A New Concept for Morphodynamic Model Validation.

Degree: 2015, Delft University of Technology

In this study, an improved error metric for morphodynamic models is introduced. Current error metrics, like the classical (Root) Mean Squared Error (RMSE), assume that predictions can be properly validated against observations by regarding the point-wise difference in bed level. This analysis inherently has the problem that phase differences of bottom features are not recognized, such that a misplaced feature is penalised twice, the so called double penalty effect. To reduce the double penalty effect, Bosboom and Reniers (2014) formulated errors metrics based on an optimal smooth displacement field between predictions and observations. Since the employed image warping technique moves pixels rather than sand, sediment continuity is not guaranteed. To overcome this limitation, this study presents an error metric based on the effective (net) sediment transport difference between prediction and observation including a recipe to compute it: the Root Mean Squared Transport Error (RMSTE). This transport difference is minimised by its 2-norm (not 1-norm) for computational efficiency. Intuitively, the RMSTE can be interpreted as a measure for the amount of work to be performed to correct the sediment displacements between prediction and observation. For predictions with equal RMSE, the highest RMSTE is awarded to the one with bed level differences with the largest wave lengths in the sense of Fourier components. This study presents case studies showing a gradual response of the RMSTE to increasing displacement of features. The double penalty effect does not occur if the distance to the boundaries is large in comparison to the feature displacement. Further research should point out if the double penalty effect occurs with real model studies and leads to counter-intuitive behaviour of the RMSTE. The transport difference field and displacement field provide opportunity for a more insightful visual comparison of prediction and observation. Filtering on displacement magnitudes may be useful in subsequent research to asses model skill at smaller scales.

Hydraulic Engineering & Water Resources Management (double degree TUD/NUS)

Hydraulic Engineering

Civil Engineering and Geosciences

Advisors/Committee Members: Stive, M.J.F. (mentor), Bosboom, J. (mentor), Reniers, A.J.H.M. (mentor), De Valk, C.F. (mentor), Yuan, J. (mentor).

Subjects/Keywords: model validation; mean squared error; double penalty effect; morphodynamics; root mean squared transport error; effective transport difference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mol, M. (. (2015). The Effective Transport Difference: A New Concept for Morphodynamic Model Validation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:aa35b28a-8bca-4b33-b99c-05a79e9154a6

Chicago Manual of Style (16th Edition):

Mol, M (author). “The Effective Transport Difference: A New Concept for Morphodynamic Model Validation.” 2015. Masters Thesis, Delft University of Technology. Accessed September 26, 2020. http://resolver.tudelft.nl/uuid:aa35b28a-8bca-4b33-b99c-05a79e9154a6.

MLA Handbook (7th Edition):

Mol, M (author). “The Effective Transport Difference: A New Concept for Morphodynamic Model Validation.” 2015. Web. 26 Sep 2020.

Vancouver:

Mol M(. The Effective Transport Difference: A New Concept for Morphodynamic Model Validation. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Sep 26]. Available from: http://resolver.tudelft.nl/uuid:aa35b28a-8bca-4b33-b99c-05a79e9154a6.

Council of Science Editors:

Mol M(. The Effective Transport Difference: A New Concept for Morphodynamic Model Validation. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:aa35b28a-8bca-4b33-b99c-05a79e9154a6


Delft University of Technology

2. Bosboom, J. Quantifying the quality of coastal morphological predictions.

Degree: 2020, Delft University of Technology

<p class="MsoNormal" style="mso-pagination:none;mso-layout-grid-align:none; text-autospace:none">This thesis investigates the behaviour of the often used point-wise skill score, the MSESSini a.k.a. BSS, and develops new error metrics that, as opposed to point-wise metrics, take the spatial structure of morphological patterns into account. The MSESSini measures the relative accuracy of a morphological prediction over a prediction of zero morphological change, using the mean-squared error (MSE) as the accuracy measure. The main findings about the MSESSini are: 1) a generic ranking, based on values for MSESSini, has limited validity, since the zero change reference model fails to make model performance comparable across different prediction situations; 2) the combination of larger, persistent and smaller, intermittent scales of cumulative change may lead to an increase of skill with time, without the prediction on either of these scales becoming more skilful with time; 3) in the presence of inevitable location errors, the MSESSini favours predictions that underestimate the variance of cumulative bed changes and 4) existing methods to correct for measurement error are inconsistent in either their skill formulation or their suggested classification scheme. In order to overcome the inherent limitations of point-wise metrics, three novel diagnostic tools for the spatial validation of 2D morphological predictions are developed. First, a field deformation or warping method deforms the predictions towards the observations, minimizing the squared point-wise error. Error measures are formulated based on both the smooth displacement field between predictions and observations and the residual point-wise error field after the deformation. In contrast with the RMSE, the method captures the visual closeness of morphological patterns. Second, an optimal transport method defines the distance between predicted and observed morphological fields in terms of an optimal sediment transport field. The optimal corrective transport field moves the misplaced sediment from the predicted to the observed morphology at the lowest quadratic transportation cost. The root-mean-squared value of the optimal transport field, the RMSTE, is proposed as a new error metric. As opposed to the field deformation method, the optimal transport method is mass-conserving, parameter-free and symmetric. The RMSTE, unlike the RMSE, is able to discriminate between predictions that differ in the misplacement distance of predicted morphological features. It also avoids the consistent reward of the underestimation of morphological variability that the RMSE is prone to. Third, a scale-selective validation approach allows any metric to selectively address multiple spatial scales. It employs a smoothing filter in such a way that, in addition to the domain-averaged statistics, localized validation statistics and maps of prediction quality are obtained per scale (geographic extent or areal size of focus). The employed skill score weights how well the morphological structure and… Advisors/Committee Members: Reniers, A.J.H.M., Stive, M.J.F., Delft University of Technology.

Subjects/Keywords: (root)-mean-squared error; model accuracy; morphodynamic modelling; model validation; optimal transport; Monge–Kantorovich; root-mean-squared transport error; effective transport difference; image warping; image matching; scale-selective validation; optical flow; Brier skill score; model skill; zero change model; measurement error; location error; pattern skill

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bosboom, J. (2020). Quantifying the quality of coastal morphological predictions. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

Chicago Manual of Style (16th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Doctoral Dissertation, Delft University of Technology. Accessed September 26, 2020. http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

MLA Handbook (7th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Web. 26 Sep 2020.

Vancouver:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Internet] [Doctoral dissertation]. Delft University of Technology; 2020. [cited 2020 Sep 26]. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

Council of Science Editors:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Doctoral Dissertation]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

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