Using a genetic algorithm to improve oil spill prediction.

Abstract:

:The performance of oil spill models is strongly influenced by multiple parameters. In this study, we explored the ability of a genetic algorithm (GA) to determine optimal parameters without the need for time-consuming manual attempts. An evaluation function integrating the percentage of coincidence between the predicted polluted area and the observed spill area was proposed for measuring the performance of a Lagrangian oil particle model. To maximise the objective function, the oil spill was run numerous times with continuously optimised parameters. After many generations, the GA effectively reduced discrepancies between model results and observations of a real oil spill. Subsequent validation indicated that the oil spill model predicted oil slick patterns with reasonable accuracy when equipped with optimal parameters. Furthermore, multiple objective optimisation for observations at different times contributed to better model performance.

journal_name

Mar Pollut Bull

authors

Guo W,Jiang M,Li X,Ren B

doi

10.1016/j.marpolbul.2018.07.026

subject

Has Abstract

pub_date

2018-10-01 00:00:00

pages

386-396

eissn

0025-326X

issn

1879-3363

pii

S0025-326X(18)30504-6

journal_volume

135

pub_type

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