Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision support systems.

中文翻译：

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探索基于机器学习的洪灾传感器不可知的破坏后果预测

近来，基于洪水传感器的结果，已经在船上应用了渐进式洪水模拟来支持紧急情况下的决策。但是，现有客船船队中只有一小部分装有洪水传感器。为了简化在旧船上安装紧急决策支持系统的操作，建议使用与洪水传感器无关的解决方案以减少改造成本。在这项工作中，采用经过渐进式洪水模拟数据库训练的机器学习算法来评估破坏情景的主要后果（最终命运，被淹没的车厢，淹没时间）。除其他方法外，这里仅使用船舶浮动位置（脚跟，纵倾和沉没）的时间演变作为预测因子来测试几种分类技术。所提出的方法已经被应用于显示出有希望的结果的箱形驳船。应用袋装决策树和加权k最近邻获得的令人鼓舞的结果表明，这种新方法可以作为新一代机载决策支持系统的基础。