# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 6 Evaluation
### Quantitative Evaluation  
A real-world dataset was taken into consideration for the quantitative evaluation and responsiveness of the suggested reasoning framework. The dataset was produced during the demonstration of the reasoning framework which took place in different pilot areas on behalf of the SAFERS [54] European-funded project and includes weather and forecasted anomalies of FWI, fine fuel moisture, duff moisture, and drought, polygons, and multipolygons for the studied regions (Corsica, Central Macedonia, Catalonia, and Piedmont).  
To measure the response times for every region, the number of polygons and multipolygons for each area was needed, as each region has its own fire management areas. For this reason, we collect 17 polygons for the Corsica area, 18 multipolygons for Central Macedonia, 42 polygons and multipolygons for Catalonia, and 56 multipolygons for Piedmont. For every fire management area, the ONTO-SAFE framework populates the ONTO-SAFE ontology with weather-related data which are received as input from weather forecast-related services. The ONTO-SAFE knowledge graphs consist of 61K statements in Knowledge Base Repository. The reasoning semantic analysis process starts after the population of ontology, as the proper queries are executed in ontology and the relative recommendations are retrieved. The reasoning response time for every region and the number of recommendations that are produced after the execution of the semantics queries are depicted in Figure 11.  
Figure 11: Reasoning response time for every region.  
The performance of the ONTO-SAFE reasoning module was characterized positively by end users as the response time, according to the number of polygons, was increased but in a perfectly reasonable manner. The reason is that the more polygons that have to be examined, the more queries will be executed to the knowledge base. Moreover, the increment in the quantity of generated recommendations also leads to a minimal impact on response time relative to the number of polygons and multipolygons.  
Furthermore, in order to improve the performance and efficiency of the ONTO-SAFE framework, we used the Douglas-Peucker algorithm to simplify polygons associated with pilot forest fire areas. This algorithm effectively reduces the complexity of the polygons while preserving their essential shape and characteristics. As a direct outcome of this simplification, we were able to optimize the processing time of our framework, ONTO-SAFE. To evaluate the framework's processing time and scalability, we conducted a testing process that involved applying the reasoning semantic analysis to four distinct forest areas. We measured the processing time in seconds (s) both before and after implementing the Douglas-Peucker algorithm, and the results are summarized in Table 5.  
It is worth noting that the observed reduction in polygon complexity has significantly expedited the analysis and decision-making procedures, especially in emergency situations. This improvement plays a vital role in enhancing the overall effectiveness of firefighting efforts.