A Brief Review of Machine Learning Algorithms in Forest Fires Science
Source abstract
Abstract
Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fire effects on ecosystems · Fire Detection and Safety Systems · Fire dynamics and safety research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Physical Geography, Institute of Geographical Sciences, Free University Berlin, Malteserstraße 74-100, 12449 Berlin, Germany
College of Computer & Information Systems, Umm al Qura University, Makkah 24382, Saudi Arabia
Funding
Free University Berlin
References (100+)
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