Real-Time Wildfire Detection and Alerting with a Novel Machine Learning Approach

Up until the end of July 2022, there have been over 38k wildfires in the US alone, decimating over 5.6 million acres. Wildfires significantly contribute to carbon emission, which is the root cause of global warming. Research has shown that artificial intelligence already plays a very important role in wildfire management, from detection to remediation. In this investigation a novel machine learning approach has been defined for spot wildfire detection in real time with high accuracy. The research compared and examined two different Convolutional Neural Network (CNN) approaches. The first approach; a novel machine learning method, a model server framework is used to serve convolutional neural network models trained for daytime and nighttime to validate and feed wildfire images sorted by different times of day. In the second approach that has been covered by existing research, one big CNN model is described as training all wildfire images regardless of daytime or nighttime. With the first approach, a higher detection precision of 98% has been achieved, which is almost 8% higher than the result from the second approach. The novel machine learning approach can be integrated with social media channels and available forest response systems via API’s for alerting to create an automated wildfire detection system in real time. This research result can be extended by fine tuning the CNN network model to build wildfire detection systems for different regions and locations. With the rapid development of network coverage such as Starlink and drone surveillance, real time image capturing can be combined with this research to fight the increasing risk of wildfires with real time wildfires detection and alerting in automation.

@article{Zhang2022,
  title     = {Real-Time Wildfire Detection and Alerting with a Novel Machine Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Audrey Zhang and Albert S. Zhang},
  doi       = {10.14569/IJACSA.2022.0130801},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130801}
}