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Fighting Wildfires with Technology: From Prediction to Detection
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Wildfire monitoring has two related but different tasks: estimating where a fire is likely to occur and detecting one that has already begun. This short overview looks at how machine-learning risk maps, satellites, ground-based sensors, AI cameras and drones can contribute to earlier warnings. None works reliably in every setting. Used together, however, they may help communities respond sooner, provided the system can cope with false alarms, limited power and unreliable communication.
Keywords
wildfire prediction; early detection; Internet of Things; remote sensing; artificial intelligence
References
[1] Agence France-Presse (2026). Indonesia haze spikes Singapore pollution, shuts Timor Leste schools. The Jakarta Post, 14 October 2026. https://www.thejakartapost.com/world/2026/10/14/indonesia-haze-spikes-singapore-pollution-shuts-timor-leste-schools
[2] Bruno AG, Moore DP, Harrison JJ, Graham A, Chipperfield MP, Vigouroux C (2026). Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events. Atmospheric Chemistry and Physics, 26(17), 12925–12952. https://doi.org/10.5194/acp-26-12925-2026
[3] Catargiu C, Cleju N, Ciocoiu IB (2024). A comparative performance evaluation of YOLO-type detectors on a new open fire and smoke dataset. Sensors, 24(17), 5597. https://doi.org/10.3390/s24175597
[4] Coffield SR, McCabe TD, Schroeder W, Chen Y, Scholten RC, Orland E, Liu T, Wiggins E, Randerson JT, Follette-Cook M, Morton DC (2026). Leveraging additional VIIRS information to improve wildfire tracking in the western US. Remote Sensing of Environment, 334, 115156. https://doi.org/10.1016/j.rse.2025.115156
[5] Ejaz N, Choudhury S (2025). A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment. Ecological Informatics, 90, 103325. https://doi.org/10.1016/j.ecoinf.2025.103325
[6] Giannakidou S, Radoglou-Grammatikis PI, Lagkas T, Argyriou V, Goudos SK, Markakis EK, Sarigiannidis PG (2024). Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey. Internet of Things, 26, 101171. https://doi.org/10.1016/j.iot.2024.101171
[7] Khan AH, Bahar AN, Wahid K (2026). A review on forest fire detection techniques: Past, present, and sustainable future. Sensors, 26(5), 1609. https://doi.org/10.3390/s26051609
[8] Makdee S, Sangkaphet P, Boonprasom C, Chaleamwong B, Chansiri N (2026). The development of a wildfire early warning system using LoRa technology. Computers, 15(2), 105. https://doi.org/10.3390/computers15020105
How to cite this paper
@article{1724389,
author = {Mohammed Fasih Uddin Adnan},
title = {Fighting Wildfires with Technology: From Prediction to Detection},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4686-4689},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724389.pdf},
abstract = {Wildfire monitoring has two related but different tasks: estimating where a fire is likely to occur and detecting one that has already begun. This short overview looks at how machine-learning risk maps, satellites, ground-based sensors, AI cameras and drones can contribute to earlier warnings. None works reliably in every setting. Used together, however, they may help communities respond sooner, provided the system can cope with false alarms, limited power and unreliable communication.},
keywords = {wildfire prediction; early detection; Internet of Things; remote sensing; artificial intelligence},
month = {October},
}