A recent project of Fire Sensors For Safer Urban Communities by Red Cross In informal settlements of Kenya and South Africa introduced modern sensor technology. This technology aims to provide early fire outbreaks detection and warning systems that would foster better fire management strategies to mitigate its impact. This project aimed at strengthening the existing traditional firefighting methods in informal settlements and provide better fire handling techniques. (Fire Sensors for Urban Communities, 2016). The critical aspect and function of the installed sensors are to provide early fire detection, distinguish between smokes and flames and eventually sound alarms across the fire affected area through SMS and broadcast to ensure that all the residents are aware of the fire outbreaks.
Additionally, the fire sensor system notifies various professional stakeholders, such as firefighters and the medical team, by sending GPS locations. The project has successfully installed 2000 sensors in households in the Mukuru slum in Nairobi and Khayelitsha slum in South Africa. This project also constituted the distribution of fire-resistant paint and building materials to help curb the adverse effects of fire. This sensor technology is better placed in handling slum fires in urban areas across Africa since it is user friendly with crucial safety features that can be upscaled to include the management of other hazards such as floods and landslides that are currently facing the vulnerable the African countries.
Other fire models in Africa are mainly focused on reducing forest fire occurrence and spread. For instance, a study was carried out in Africa to quantify the drivers and predictability of seasonal changes in the fire, mainly in forest ecosystems (Mao et al., 2020). The study employed a Stepwise Generalised Equilibrium Feedback Assessment (SGEFA) and Machine Learning Techniques (MLTS). MLTS is a tool that investigates human-related and natural factors that influence fire activity (Andela et al., 2017). The study focused on investigating the main mechanisms and drivers of fire evolution. In the study, different environmental factors were evaluated. This combined method of the SGEFA-MLT approach was found to be useful in predicting fire occurrence one month before the incident and it can be used generalize seasonal estimates of fire risk universally.
- J. Roberts and M. J. Wooster, “Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1200-1218, April 2008, doi: 10.1109/TGRS.2008.915751.
Yu, Y., Mao, J., Thornton, P. E., Notaro, M., Wullschleger, S. D., Shi, X., … & Wang, Y. (2020). Quantifying the drivers and predictability of seasonal changes in African fire. Nature Communications, 11(1), 1-8..
Andela, N. et al. A human-driven decline in the global burned area. Science 356, 1356–1362 (2017)
Fire sensors for urban communities.Retrieved from https://www.rcrcmagazine.org/2016/04/bright-ideas-local-solutions/ 2016