Post-disaster flight risk modeling with unmanned aircraft system (Uas) relay communications: An object-oriented bayesian network approach

As a natural disaster can leave affected areas in a state of distress and disorder, the use of UAS technology could aid in post-disaster visual management and monitoring. Although UAS technology is a key factor in disaster relief and recovery for humanitarian practices, there is an increased risk in the National Airspace System (NAS) when more unmanned aerial technology is integrated with other airborne technology. A notional scenario is modeled after Hurricane Maria, the natural disaster that occurred in September 2017 that had long lasting effects in Puerto Rico. This scenario utilizes UAS technology that can survey the post-hurricane scenario through a relay communication system that incorporates visual-line-of-sight (VLOS) and beyond-visual-line-of-sight (BVLOS) surveying. This relay scenario is modeled by using Object-Oriented Bayesian Networks (OOBNs) to illustrate the interaction between four hazard clusters as described by the Hazard Classification and Analysis System (HCAS), including the Unmanned Aircraft System (UAS), Environment, Operations, and the Hurricane Response Helicopter (HRH). Additionally, a human factors perspective is integrated into this model by applying the Human Factors Analysis and Classification System (HFACS) to the UAS and HRH. The safety risk regarding the incorporation of the UAS relay technology is assessed through analyzing the likelihood of a Near Mid-Air Collision (NMAC). It is found from this model that with evidence of the presence of one or more of the four hazards, the likelihood of an NMAC increases.