Location of disaster assessment UAVs using historical tornado data

Preparing and responding to disasters is a complicated task. One must balance coverage of SAR resources versus preparation cost. This article presents a method and solution to prepositioning UAV damage assessment and search teams in Oklahoma using historical tornado data. The approach is based on set covering and multi-station vehicle routing models. It also presents a method to robustify the solution in the event a UAV team cannot be activated to respond to the disaster. This can simulate a team unable to respond. Results show 70% more stations and teams being required when chance of a depot failure goes from 0 to 5% and 90% more stations required when 0-10%. We find that when trying to use a solution that does not account for depot failure, the system of UAVs cannot meet search completion targets in 3-4% of cases. These results demonstrate accounting for the chance of teams not being able to respond to domestic disasters is important and failing to do so means an increased chance of not being able to respond adequately to disasters and incorporating the chance of station failure has a profound impact on the number of stations needed.

Automated summary

This article presents a method to preposition UAV damage assessment and search teams in Oklahoma using historical data. The results show the importance of considering the chance of teams not being able to respond and the impact of station failure on the number of stations needed.