The project is responsible for obtaining satellite and geographic data and cross-referencing trends data in the network. Using data analysis and learning algorithms, you can visualize in real time the most fire-prone areas and ongoing fire zones. Applicable to other types of disasters such as landslides, floods, earthquakes, etc.), which according to fire range or radius suggest the nearest places of water reserves, evacuation routes, available rescue routes, estimating locations of possible Post-fire, useful fire-extinguishing information quickly, efficiently and effectively, as well as suggesting measures to control the spread and control of fire and thus help with valuable information to the population, firefighters, police, military and rescue forces during the fire. Once the disaster ends all the information will be recorded in a database for further analysis and study. This information will also be very useful in giving feedback to the learning algorithm and fine-tuning it to be more efficient in the analysis for subsequent disasters.
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A wildfire is the fire that spreads in forest or wild lands affecting the vegetation, flora and fauna. The speed at which it spreads causes both extreme health hazards immediate hazards due to smoke pollution and its ability to overcome obstacles such as roads, rivers and firewalls. Therefore, our team proposes the Fire Fighter tool.
FireFighter has technologies such as machine learning that is responsible for analyzing satellite and geographic data and crossing them with trends in the network using a learning algorithm can visualize in real time the areas most prone to fires.
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