Naslov
Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

Autori
Braidotti, Luca ; Valčić, Marko ; Prpić-Oršić, Jasna

Izvornik
Journal of marine science and engineering (2077-1312) 9 (2021), 3; 271, 20

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
damaged ship ; progressive flooding ; decision trees ; KNN ; SVM ; decision support system

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Brodogradnja, Tehnologija prometa i transport, Informacijske i komunikacijske znanosti

POVEZANOST RADA