## 5 Testing on Historical Data
### Data and Creation of the Historical Cases  
The data was pulled from the historical meteorological data from March 2008 to June 2019 (inclusive) for the Weather Forecast Offices with the ID code of: OUN, TSA, AMA, SHV, LZK, LCH, LIX, JAN, MEG, HUN, BMX, MOB, FFC, TAE, JAX, CHS, PAH, OHX, MRX, LMK, JKL - which encompasses the US States of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, Oklahoma, South Carolina, Tennessee, and Texas (A map and list of WFO offices can be found at [[https://www.weather.gov/srh/nwsoffices](https://www.weather.gov/srh/nwsoffices)]([https://www.weather.gov/srh/nwsoffices](https://www.weather.gov/srh/nwsoffices)), accessed on 1 February 2023). This and the location of all data used in this paper are in Appendix B in Table 13. There were 182 days with tornadoes in this region. Of these 182 days, several had multiple tornadoes in multiple areas. If we separate these instances where multiple tornadoes occurred on the same day, we find there are 415 unique cases. We applied the procedure explained before. The first analysis is testing the efficacy of the method to identify the extent of the damage, again the distance the UAV took from identifying the first damaged waypoint to the last damaged waypoint divided by the minimum Hamiltonian path to identify all the damaged waypoints. The table summarizing these results can be found in Table 14.  
It is immediately apparent that: (1) data-driven-first and symmetric influence matrices continue to be better than using data driven; (2) the lack of an initial route is much more positively impactful in the historical cases than in the artificial cases; and (3) there is very little difference in the scores of several pairs of cases. \begin{table}
\begin{tabular}{|c c c c|c|} \hline \hline
**Initial Route?** & **Matrix Type** & **MStC** & **Mean Score** &
\begin{tabular}{c} **Score** \\ **Finish Route** \\ \end{tabular} \\ \hline TRUE & symmetric & 0.20 & 3.3282 & 36 269 \\ \hline TRUE & data-driven & 0.20 & 3.8860 & 36.619 \\ \hline FALSE & data-driven & 0.20 & 3.9305 & 38.403 \\ \hline FALSE & symmetric-first & 0.20 & 4.0516 & 38.469 \\ \hline TRUE & symmetric & 0.10 & 3.4422 & 40.480 \\ \hline FALSE & data-driven-first & 0.20 & 3.4128 & 41.301 \\ \hline FALSE & symmetric & 0.20 & 3.4329 & 41.349 \\ \hline TRUE & data-driven & 0.10 & 3.8732 & 43.395 \\ \hline FALSE & symmetric-first & 0.10 & 3.9703 & 48.069 \\ \hline FALSE & data-driven & 0.10 & 4.0058 & 48.101 \\ \hline FALSE & data-driven-first & 0.10 & 3.2596 & 49,230 \\ \hline FALSE & symmetric & 0.10 & 3.3883 & 49,263 \\ \hline TRUE & symmetric & 0.00 & 3.4198 & 50.218 \\ \hline FALSE & symmetric & 0.00 & 3.4642 & 60.880 \\ \hline TRUE & data-driven & 0.00 & 3.9545 & 60.895 \\ \hline FALSE & data-driven-first & 0.00 & 3.3695 & 60.926 \\ \hline FALSE & symmetric-first & 0.00 & 4.1296 & 64.801 \\ \hline FALSE & data-driven & 0.00 & 4.0698 & 65.037 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Scores of finishing the route. Next, we assess the quality of the dynamic program to identify the damage in historical cases and to complete its route (assert that there is no further damage to be identified). These values are computed likewise to the generated cases. To complete this analysis, the distance of the route when the damage is first uncovered is divided/normalized by the number of waypoints in the SBW (metric 1); likewise, the distance of the route when completed is divided/normalized by the number of waypoints in the SBW (metric 2). These results are shown in Tables 5 and 6. Reflecting the generated cases, the relationship between not using an initial route and how quickly damage is identified is similar between the generated cases and the historical cases (Table 5). It is also apparent, however, that the MStC is a stronger indicator of a lower score to find damage than the matrix type.