# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 5 Semantic Reasoning for Decision Support
### Decision Support for Weather and Forecast Anomalies  
Weather and climate parameters control wildfire activity by regulating biomass growth and snowpack accumulation, which affects the availability of fire fuel, or, in the near term, by affecting the moisture content of vegetation and the duff layer and, consequently, their flammability [42]. To understand the situation and allow different stakeholders to adopt and enable different early warning techniques, extreme weather forecasting information and anomalies must be integrated into the decision support system. An implementation of a decision support module that takes as input these anomalies is useful for supporting the prevention phase and enhancing the emergency management system of wildfire events. The transformation of incoming data, including JSON and GeoJSON files, into RDF format for semantic representation is the foremost action of the semantic reasoning mechanism. The CQs concerning the anomalies are translated to GeoSPARQL queries executed in the ontology to retrieve alerts and recommendations that fulfill the proper criteria. A GeoSPARQL query on how we can define the areas via specific area codes--which have any anomaly of FWI, fine fuel moisture, duff moisture, or drought--is represented in Listing 1.  
The query above retrieves information regarding the area code, the value of each anomaly, the timestamp, the region, and the polygon. All this information produces a JSON message that is visualized via a dashboard in order to inform the stakeholders to manage disaster risk better and minimize negative socio-economic impacts [43]. Figure 7 presents how the recommendations regarding the anomalies appear in Dashboard and show whether the recommendation is addressed to professionals or citizens for a specific location.  
Figure 7: Recommendations regarding the anomalies in dashboard.  
PREFIX safers: <[[http://www.semanticweb.org/certh/safers#](http://www.semanticweb.org/certh/safers#)]([http://www.semanticweb.org/certh/safers#](http://www.semanticweb.org/certh/safers#))>  PREFIX geof: <[[http://www.opengis.net/def/function/geosparql/](http://www.opengis.net/def/function/geosparql/)]([http://www.opengis.net/def/function/geosparql/](http://www.opengis.net/def/function/geosparql/))>  PREFIX ogc: <[[http://www.opengis.net/ont/geosparql#](http://www.opengis.net/ont/geosparql#)]([http://www.opengis.net/ont/geosparql#](http://www.opengis.net/ont/geosparql#))>  PREFIX uom: <[[http://www.opengis.net/def/uom/OGC/1.0/](http://www.opengis.net/def/uom/OGC/1.0/)]([http://www.opengis.net/def/uom/OGC/1.0/](http://www.opengis.net/def/uom/OGC/1.0/))>  SELECT DISTINCT?areaCode  (SAMPLE(?fwi_anomaly) as?fwi_anomaly)  (SAMPLE(?drought_anomaly) as?drought_anomaly)  (SAMPLE(?duffm_anomaly) as?duffm_anomaly)  (SAMPLE(?fim_anomaly) as?fim_anomaly)  (SAMPLE(?timestamp) as?timestamp)  (SAMPLE(?region) as?region)  (SAMPLE(?polygon) as?polygon)  WHERE {  OPTIONAL {?point safers:hasDroughtAnomaly?drought_anomaly.}  OPTIONAL {?point safers:hasDuffMoistureAnomaly?duffm_anomaly.}  OPTIONAL {?point safers:hasFWIAnomaly?fwi_anomaly.}  OPTIONAL {?point safers:hasFineFuelMoistureAnomaly?ffm_anomaly.} ?point safers:hasLatitude?lat. ?point safers:hasLongitude?lon. ?point safers:hasTimestamp?timestamp. ?point safers:hasRegion?region. ?point safers:hasLocationID?areaCode. ?point safers:hasLocation?polygon. ?point ogc:asWKT?fWKT.  }  GROUP BY?areaCode