A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

• Published in: Fire technology Vol. 57; no. 2; pp. 559 - 590
• Main Author: Abid, Faroudja
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-03-2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Characterization and Evaluation of Materials; Civil Engineering; Classical Mechanics; Ecological effects; Editors’ choice; Engineering; Fire science reviews; Forest & brush fires; Forest fires; Forests; Learning algorithms; Machine learning; Physics; Polls & surveys; Predictions; Logistic regression; Fire prediction system; Fire detection system; Neural network; Forest fires; Machine learning
• ISSN: 0015-2684; 1572-8099
• DOI: 10.1007/s10694-020-01056-z

Forest fires are one of the major environmental concerns, each year millions of hectares are destroyed over the world, causing economic and ecological damage as well as human lives. Thus, predicting such an environmental issue becomes a critical concern to mitigate this threat. Several technologies and new methods have been proposed to predict and detect forest fires. The trend is toward the integration of artificial intelligence to automate the prediction and detection of fire occurrence. This paper presents a comprehensive survey of the machine learning algorithms based forest fires prediction and detection systems. First, a brief introduction to the forest fire concern is given. Then, various methods and systems in forest fires prediction and detection systems are reviewed. Besides works that reported fire prediction and detection systems, studies that assessed the factors influencing the fire occurrence and risk are discussed. The main issues and outcomes within each study are presented and discussed.