# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 7 Discussion  
This study presents an ontology-based approach to enhance information sharing among stakeholders and facilitate advanced whereas in forest fire emergencies. The proposed approach provides a framework that uses a common, shared vocabulary and reasoning mechanism in order to assist decision-makers to make informed decisions. Related ontologies and ontology-based systems for emergencies were investigated structurally and modularly to describe the conceptual model of the underlying KGs at various levels. Several concepts and classes were reused from relative ontologies such as classes Fire, Impact, Location, Report, etc., from Empathi [23] ontology; classes Fire, Incident, WeatherConditions, and Communication from EmergencyFire [28] ontology; classes Incident, Impact, Location, Dataset, Mission, etc., from BeAWAE [32] ontology; and classes related to Topography and Vegetation from OntoFire [31] ontology. For depicting damages and their relationship to resources in order to categorize damages and resources, our strategy is based on the SoKNOS [22] approach. Not all concepts and classes from related ontologies were used due to the fact that many of them, such as EmergencyFire [28] ontology, were focused only on fire emergencies in buildings, and our target domain focuses on forest fires. In addition, BACAREX [29] ontology was not used because as a heavyweight ontology, it is complex and difficult to comprehend due to its extensive structure and a vast amount of information. Likewise, the geo-ontology [30] was not used because no evaluation of those terms was carried out to verify their semantic accuracy although it is a very interesting multi-level ontology model and its focus relies on meteorological disasters. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{\begin{tabular}{c} **Number of Polygons** \\ **and Multipolygons** \\ \end{tabular} } & \begin{tabular}{c} **Processing Time** \\ **before Using the** \\ **Douglas-Peucker** \\ **Algorithm(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Processing Time** \\ **after Using the** \\ **Douglas-Peucker** \\ **Algorithm(s)** \\ \end{tabular} \\ \hline \hline
\begin{tabular}{c} France (Corsica) \\ Greece (Central \\ Macedonia) \\ Spain (Catalonia) \\ Italy (Piedmont) \\ \end{tabular} & 17 & 12 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Processing time (s) before and after the usage of the Douglas–Peucker algorithm per area. Furthermore, in this paper, we demonstrate the analysis results of weather anomalies that were represented in different regions. Data refer to pilot regions in Corsica, Central Macedonia, Catalonia, and Piedmont. We execute the related anomalies rules on top of the ONTO-SAFE domain ontology to investigate what recommendations will actually be produced when the appropriate weather variables significantly exceed a normal level. The performance of the ONTO-SAFE reasoning module in these areas was characterized positively by end users and the evaluated results imply that the ONTO-SAFE framework can be optimized for large-scale implementation. Additionally, in order to evaluate the framework's processing time and scalability, we used the Douglas-Peucker algorithm to simplify polygons for forest fire areas. This algorithm effectively reduces the complexity of the polygons, leading to a more concise representation of the data while retaining essential information. By implementing this optimization, we have significantly enhanced the scalability of our framework, resulting in reduced time consumption for processing and handling large datasets. This optimization contributes to more efficient decision-making processes, as it enables quicker data analysis and improved resource utilization. As a result, the proposed ontology-based framework can provide support by generating recommendations in different disaster risk management phases and assisting in significant activities such as prescribed burning processes.