Risk-based, sensor-fused detection of flooding casualties for emergency response

The intrinsic complexity of the flooding process on ships renders accurate quantification of the flooding risk a highly arduous task, particularly in the context of emergency management. This is especially true for large cruise vessels where convolution stems from innovative designs and complex internal subdivision resulting in a multitude of variables and their interdependencies. This augments the uncertainty and imposes challenges on the crew in obtaining a complete overview and in making fully informed decisions. This paper presents a methodology whereby sensors and analytics are combined utilising probabilistic multi-sensor data fusion to predict the flooding extent with reduced uncertainty to facilitate informed decision-making in emergencies, forming the basis for optimised implementation of emergency response measures for vessel survival and subsequent safe return to port. The accurate prediction of flooding extent as presented, is a fundamental prerequisite for, and could be of great assistance in, decision making in emergencies, thus saving lives.