Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning (CROSBI ID 291425)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Braidotti, Luca ; Valčić, Marko ; Prpić-Oršić, Jasna
engleski
Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning
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.
damaged ship ; progressive flooding ; decision trees ; KNN ; SVM ; decision support system
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Podaci o izdanju
9 (3)
2021.
271
20
objavljeno
2077-1312
10.3390/jmse9030271
Trošak objave rada u otvorenom pristupu
Povezanost rada
Brodogradnja, Informacijske i komunikacijske znanosti, Tehnologija prometa i transport