Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields
Accurate prediction of the trajectories of material drifting on the ocean surface is critical for risk assessment and responses to environmental emergencies. Prediction of these trajectories is subject to uncertainty arising from a number of sources, with a primary source being uncertainty in the mo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/9/1752 |
| _version_ | 1797579399517700096 |
|---|---|
| author | Hauke Blanken Caterina Valeo Charles G. Hannah Usman T. Khan |
| author_facet | Hauke Blanken Caterina Valeo Charles G. Hannah Usman T. Khan |
| author_sort | Hauke Blanken |
| collection | DOAJ |
| description | Accurate prediction of the trajectories of material drifting on the ocean surface is critical for risk assessment and responses to environmental emergencies. Prediction of these trajectories is subject to uncertainty arising from a number of sources, with a primary source being uncertainty in the modelled ocean surface currents and winds used as input to the trajectory model. This article presents a fuzzy number-based algorithm for propagating uncertainty through a particle tracking scheme in a time- and space-varying velocity field. The performance of the algorithm was tested by applying it to idealized, analytical velocity fields and scoring the results against the analytical solution. Both epistemic and aleatoric uncertainty were considered and combined using a fractional Brownian motion model for temporal autocorrelation of the uncertainty. In the evaluation of the algorithm, sensitivity was quantified with respect to parameters such as timestep size, resolution of the forcing velocity field, spatial and temporal gradients in the forcing, and resolution of the applied uncertainty. Parameter values optimizing uncertainty representation and computational cost were identified. The applied uncertainty was found to evolve in agreement with classical relative dispersion relationships. |
| first_indexed | 2024-03-10T22:35:32Z |
| format | Article |
| id | doaj.art-cd043601f10943dd922f8def16830182 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T22:35:32Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-cd043601f10943dd922f8def168301822023-11-19T11:26:54ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-09-01119175210.3390/jmse11091752Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity FieldsHauke Blanken0Caterina Valeo1Charles G. Hannah2Usman T. Khan3Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC V8L 4B2, CanadaDepartment of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, CanadaInstitute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC V8L 4B2, CanadaLassonde School of Engineering, York University, Toronto, ON M3J 1P3, CanadaAccurate prediction of the trajectories of material drifting on the ocean surface is critical for risk assessment and responses to environmental emergencies. Prediction of these trajectories is subject to uncertainty arising from a number of sources, with a primary source being uncertainty in the modelled ocean surface currents and winds used as input to the trajectory model. This article presents a fuzzy number-based algorithm for propagating uncertainty through a particle tracking scheme in a time- and space-varying velocity field. The performance of the algorithm was tested by applying it to idealized, analytical velocity fields and scoring the results against the analytical solution. Both epistemic and aleatoric uncertainty were considered and combined using a fractional Brownian motion model for temporal autocorrelation of the uncertainty. In the evaluation of the algorithm, sensitivity was quantified with respect to parameters such as timestep size, resolution of the forcing velocity field, spatial and temporal gradients in the forcing, and resolution of the applied uncertainty. Parameter values optimizing uncertainty representation and computational cost were identified. The applied uncertainty was found to evolve in agreement with classical relative dispersion relationships.https://www.mdpi.com/2077-1312/11/9/1752uncertaintyfuzzy numbersparticle tracking |
| spellingShingle | Hauke Blanken Caterina Valeo Charles G. Hannah Usman T. Khan Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields Journal of Marine Science and Engineering uncertainty fuzzy numbers particle tracking |
| title | Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields |
| title_full | Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields |
| title_fullStr | Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields |
| title_full_unstemmed | Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields |
| title_short | Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields |
| title_sort | propagating particle tracking uncertainty defined by fuzzy numbers in spatially variable velocity fields |
| topic | uncertainty fuzzy numbers particle tracking |
| url | https://www.mdpi.com/2077-1312/11/9/1752 |
| work_keys_str_mv | AT haukeblanken propagatingparticletrackinguncertaintydefinedbyfuzzynumbersinspatiallyvariablevelocityfields AT caterinavaleo propagatingparticletrackinguncertaintydefinedbyfuzzynumbersinspatiallyvariablevelocityfields AT charlesghannah propagatingparticletrackinguncertaintydefinedbyfuzzynumbersinspatiallyvariablevelocityfields AT usmantkhan propagatingparticletrackinguncertaintydefinedbyfuzzynumbersinspatiallyvariablevelocityfields |