cranslik v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods
Clicks: 120
ID: 128260
2015
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
120 views
16 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #74 of 133 articles by views in international journal of quantum chemistry
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 133 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Oil spill models are used to forecast the transport and fate of oil after it
has been released. CranSLIK is a model that predicts the movement and spread
of a surface oil spill at sea via a stochastic approach. The aim of this work
is to identify parameters that can further improve the forecasting algorithms
and expand the functionality of CranSLIK, while maintaining the run-time
efficiency of the method. The results from multiple simulations performed
using the operational, validated oil spill model, MEDSLIK-II, were analysed
using multiple regression in order to identify improvements which could be
incorporated into CranSLIK. This has led to a revised model, namely CranSLIK
v2.0, which was validated against MEDSLIK-II forecasts for real oil spill
cases. The new version of CranSLIK demonstrated significant forecasting
improvements by capturing the oil spill accurately in real validation cases
and also proved capable of simulating a broader range of oil spill scenarios.
| Reference Key |
rutherford2015geoscientificcranslik
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;R. Rutherford;I. Moulitsas;B. J. Snow;A. J. Kolios;M. De Dominicis |
| Journal | international journal of quantum chemistry |
| Year | 2015 |
| DOI |
10.5194/gmd-8-3365-2015
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.