文章基本信息

标题：Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests
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作者：Luca Braidotti ; Jasna Prpić-Oršić ; Marko Valčić 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2021
卷号：9
期号：11
页码：1303
DOI：10.3390/jmse9111303
语种：English
出版社：MDPI AG
摘要：Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.