## 6 Conclusions  
This paper presents both a (1) method of attempting to identify the extent of the damaged area in the aftermath of a tornado as quickly as possible and (2) a method for creating artificial cases that can be used to increase the number of cases available for testing. We have demonstrated that intelligently incorporating historical data into the decision-making process can assist the dynamic routing method to route and reroute a damage assessment UAV to determine the extent of damage as quickly as possible. In addition, we have developed a method for creating artificial instances that react similarly for key goals (that is, identifying the extent of tornado damage). The difficulty we encountered was that we successfully developed the algorithm to use only one UAV in identifying damage (rather than multiple UAVs in the previous literature). These artificial instances perform similarly in some important metrics to the historical database of tornado instances that we produced; however, there is a gap that needs to be addressed. From the outset, we aimed to create a method to \"Minimize the distance/time it takes to uncover extent of tornado\" and the generated cases perform similarly. Recall this was our primary goal. However, the other goals outlined in this paper have slight differences between parameters in the historical cases data set versus the generated cases data set. For instance, using a data-driven influence matrix first helps identify damage in the generated cases, but this has less of an impact on the actual historical data set. This is possibly because the number of waypoints is larger than the generated cases. The nature of the data driven matrix strongly encourages the UAV to travel perpendicular to the typical direction of tornadoes before damage is beginning to be identified. That is, before damage is identified, the influence matrix encourages the UAV to fly in a northwest-to-southeast direction. This makes intuitive sense as, if you are trying to initially identify something that will typically be in a northeast-to-southwest orientation, you would systematically search in a northwest-to-southeast direction. The additional waypoints and size of the search area curtails this effect. Second, we also show that the use of historical tornado trends has an impact on the response method outlined in this article. The method is interesting as the historical data typically has a positive impact on the discovery of damaged areas and a reduction in the standard deviation of uncovering damaged areas. That is, once the damage has begun to be uncovered, the information gained from the route prior to identifying damage can help (typically) reduce the extremes in time/distance it takes identifying the extent of the damage. However, using the data driven matrix after damage is identified performs worse regardless of any other parameters used. Tornadoes are broadly considered to be a random and unpredictable phenomenon that are hard to predict and simulate. This paper demonstrates that, even given the unpredictable nature of tornadoes, the intelligent and directed use of historical tornado damage can help rescuers identify the area impacted by a tornado in a much more efficient manner. Some main limitations and avenues for future research to still be explored include further sensitivity on the setup of the influence matrix (as a larger range of influence may be desired for spatially larger cases). The method outlined here is also ripe for introducing more real-time data into the method. For example: the press, storm chasers, or emergency calls reporting impacted areas can convert a waypoint with unknown attributes into a known waypoint, which can be updated immediately in the dynamic routing algorithm. We use one UAV to assess an area; practically, multiple UAVs can reduce the time it takes to search the entire area, and as indicated in the previous literature, when flying multiple UAVs over a disaster area the UAV with the greatest time aloft is what we attempt to minimize [11].