Development of an Integrated Decision Support System for Supporting Offshore Oil Spill Response in Harsh Environments
Notice bibliographique
Résumé
Offshore oil spills can lead to significantly negative impacts on socio-economy and constitute a direct hazard to the marine environment and human health. The response to an oil spill usually consists of a series of dynamic, time-sensitive, multi-faceted and complex processes subject to various constraints and challenges. In the past decades, many models have been developed mainly focusing on individual processes including oil weathering simulation, impact assessment, and clean-up optimization. However, to date, research on integration of offshore oil spill vulnerability analysis, process simulation and operation optimization is still lacking. Such deficiency could be more influential in harsh environments. It becomes noticeably critical and urgent to develop new methodologies and improve technical capacities of offshore oil spill responses. Therefore, this proposed research aims at developing an integrated decision support system for supporting offshore oil spill responses especially in harsh environments (DSS-OSRH). Such a DSS consists of offshore oil spill vulnerability analysis, response technologies screening, and simulation-optimization coupling. The uncertainties and/or dynamics have been quantitatively reflected throughout the modeling processes. First, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) approach has been developed. A real-world case study was applied for offshore oil spill vulnerability index (OSVI) classification in the south coast of Newfoundland to demonstrate this approach. Furthermore, a Monte Carlo simulation based integrated rule-based fuzzy adaptive resonance theory mapping (MC-IRFAM) approach has been developed for screening and ranking for spill response and clean-up technologies. The feasibility of the MC-IRFAM was tested with a case of screening and ranking response technologies in an offshore oil spill event. A novel Monte Carlo simulation based dynamic mixed integer nonlinear programming (MC-DMINP) approach has also been developed for the simulation-optimization coupling in offshore oil spill responses. To demonstrate this approach, a case study was conducted in device allocation and oil recovery in an offshore oil spill event. Finally, the DSS-OSRH has been developed based on the integration of MC-TSAM, MC-IRFAM, AND MC-DSINP. To demonstrate its feasibility, a case study was conducted in the decision support during offshore oil spill response in the south coast of Newfoundland. The developed approaches and DSS are the first of their kinds to date targeting offshore oil spill responses. The novelty can be reflected from the following aspects: 1) an innovative MC-TSAM approach for offshore OSVI classification under complexity and uncertainty; 2) a new MC-IRFAM approach for oil spill response technologies classification and ranking with uncertain information; 3) a novel MC-DMINP simulation-optimization coupling approach for offshore oil spill response operation and resource allocation under uncertainty; and 4) an innovational DSS-OSRH which consists of the MC-TSAM, MC-IRFAM, MC-DMINP, supporting decision making throughout the offshore oil spill response processes. These methods are particularly suitable for offshore oil spill responses in harsh environments such as the offshore areas of Newfoundland and Labrador (NL). The research will also promote the understanding of the processes of oil transport and fate and the impacts to the affected offshore and shoreline area. The methodologies will be capable of providing modeling tools for other related areas that require timely and effective decisions under complexity and uncertainty.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».