Susmitha, I., Femila, R.J. and Sivasamy, V. (2022) Detection of Forest Fire Using Support Vector Machine in Comparison with Random Forest to Measure Accuracy, Precision and Recall. 2022 International Conference on Cyber Resilience (ICCR), Dubai, 6-7 October 2022, 1-6.
https://doi.org/10.1109/ICCR56254.2022.9995895

has been cited by the following article:

• TITLE: A Survey of the Machine Learning Models for Forest Fire Prediction and Detection

  AUTHORS: Prathibha Sobha, Shahram Latifi

  KEYWORDS: AI, Computer Vision, Deep Learning, Forest Fires, ML, UAV

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.16 No.7, July 27, 2023

  ABSTRACT: Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.