FireAnalyst: An effective system for detecting fire geolocation and fire behavior in forests using mathematical modeling

This study proposes an effective new fire detection method and monitoring software for an early-warning fire detection systemaimed at valuable forested areas, such as botanical parks or high conservation value forests, particularly those with boundaries. Thesecritical forested areas need to be appropriately managed because they contain large concentrations of biological diversity, includingthreatened or endangered species, which are very susceptible to fire outbreaks; thus, early detection of fire and rapid response have avery important place in the fight against fire in those areas. In this proposed system, special detectors with state-of-the-art multispectralinfrared technology and mathematical modeling algorithms have been utilized to create a smart fire detection system that can detectfires at a very early stage. The geolocation and behavior of emerging fires in a forest are also estimated with maximum spatial resolutionby superimposing the detection areas of multispectral infrared detectors. In this study, candidate fire regions are examined for feasibilityfirst. Next, the most suitable fire detector type is determined and used for expanding the fire control area, to have the highest positionalaccuracy in estimating the location of an emerged fire. Thereafter, mathematical models for the positioning of the detectors are createdto have high spatial resolution in detecting the coordinates of forest fires by using the libraries of Google Maps APIs in the cloud. Thegeolocation of the fire and behavior of the fire inside the model are then simulated visually on the map portal, thanks to an extraordinarystandalone software program called FireAnalyst. The proposed system was implemented for the Faruk Yalçın Zoo and Botanical Parkin Darıca, Turkey. Experimental results have indicated that monitoring fires with FireAnalyst using selected multispectral infrareddetectors positioned toward the center geometry outperformed other fire monitoring systems, providing a significantly shortened firedetection timeframe and high spatial resolution (up to 4.5 m) in detecting the geolocation of a fire in a minimum of ~3599.56 m2forested area, and it adds functionality, such as real-time fire behavior analysis (spreading speed of fire, spreading direction).

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Alkhatib AAA (2014). A review on forest fire detection techniques. International Journal of Distributed Sensor Networks 10 (3): 97368. doi: 10.1155/2014/597368
Bogue R (2013). Sensors for fire detection. Sensor Review 33 (2): 99-103. doi: 10.1108/02602281311299635
Brown E, Dudley N, Lindhe A, Muhtaman DR, Stewart C et al. (2013). Common Guidance for the Identification of High Conservation Values. Bonn, Germany: HCV Resource Network.
Cetin AE, Merci B, Gunay O, Toreyin BU, Verstockt S (2016). Methods and Techniques for Fire Detection. Amsterdam, the Netherlands: Academic Press.
Chen X, Dong F (2016). A dual-band flame detector based on video. Optik 127 (1): 478-483. doi: 10.1016/j.ijleo.2015.09.238
Chopde NR, Nichat MK (2013). Landmark based shortest path detection by using A* and Haversine formula. International Journal of Innovative Research in Computer and Communication Engineering 1 (2): 298-302. doi: 10.15680/ IJIRCCE
Conejar RJ, Kim H (2014). A medical decision support system (DSS) for ubiquitous healthcare diagnosis system. International Journal of Software Engineering and Its Applications 8 (10): 237-244.
Erden F, Toreyin BU, Soyer EB, Inac I, Gunay O et al. (2012). Wavelet based flickering flame detector using differential PIR sensors. Fire Safety Journal 53: 13-18. doi: 10.1016/j. firesaf.2012.06.006
Eugenio FC, Santos AR, Fiedler NC, Ribeiro GA, Silva AG et al. (2016). GIS applied to location of fires detection towers in domain area of tropical forest. Science of the Total Environment 562: 542-549. doi: 10.1016/j.scitotenv.2016.03.231
Ganesh UA, Anand M, Arun S, Dinesh M, Gunaseelan P et al. (2013). Forest fire detection using optimized solar-powered Zigbee wireless sensor networks. International Journal of Scientific & Engineering Research 4 (6): 586-596.
Gardiner B, Ahmad W, Cooper T (2011). Collision Avoidance Techniques for Unmanned Aerial Vehicles. Auburn, AL, USA: Auburn University.
Kasie FM (2013). Combining simple multiple attribute rating technique and analytical hierarchy process for designing multicriteria performance measurement framework. Global Journal of Researches in Engineering Industrial Engineering 13 (1): 15-30.
Ko BC, Cheong K, Nam J (2009). Fire detection based on vision sensor and support vector machines. Fire Safety Journal 44 (3): 322-329. doi: 10.1016/j.firesaf.2008.07.006
Mare GD, Granata MF, Nestico A (2015). Weak and strong compensation for the prioritization of public investments: multidimensional analysis for pools. Sustainability 7 (12): 16022-16038. doi: 10.3390/su71215798
Mathews S, Ellis P, Hurle JH (2010). Evaluation of Three Systems. Melbourne, Australia: Australia Bushfire Cooperative Research Centre.
Monedero S, Ramirez J, Cardila A (2019). Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction. Ecological Modelling 392 (24): 103-107. doi: 10.1016/j.ecolmodel.2018.11.016
Risawandi, Rahim R (2016). Study of the simple multi-attribute rating technique for decision support. International Journal of Scientific Research in Science and Technology 2 (6): 491-494.
Sun J, Guo G, Zhang X (2014). Research on UV flame detector. In: 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, 18–20
September 2014, Harbin, China. Piscataway, NJ, USA: IEEE, pp. 135-138.
Truong TX, Kim J (2012). Fire flame detection in video sequences using multi-stage pattern recognition techniques. Engineering Applications of Artificial Intelligence 25 (7): 1365-1372. doi: 10.1016/j.engappai.2012.05.007
Wu H, Wu D, Zhao J (2019). An intelligent fire detection approach through cameras based on computer vision methods. Process Safety and Environmental Protection 127: 245-256. doi: 10.1016/j.psep.2019.05.016
Zawodnik A, Niewada M (2018). Multiple criteria decision analysis (MCDA) for health care decision making – overview of guidelines. Journal of Health Policy & Outcomes Research 2: 1-12. doi: 10.7365/JHPOR.2018.2.4