Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Predicting Maritime Groundings Using Support Vector Data Description Model
verfasst von : A. G. Rekha, Loganathan Ponnambalam, Mohammed Shahid Abdulla
Erschienen in: Computational Intelligence and Intelligent Systems
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
This paper focuses on grounding prediction related to sea vessels. Grounding accidents are one of the most common causes for ship disasters. Hence, there is a growing need to assess and analyze probabilities as well as related consequences of ship running aground. Using a real world marine incident dataset obtained from the United States Coast Guard National Response Center, we have demonstrated that Support Vector Data Description based methods can be successfully used for grounding prediction. After preprocessing the raw data, a total of 15165 incidents were obtained out of which there were 291 cases of ship running aground and was used in our study. A prediction accuracy of 98.25 % was achieved using the Lightly Trained Support Vector Data Description.