Open Access
Open access
Applied Sciences (Switzerland), volume 13, issue 14, pages 8275

A Brief Review of Machine Learning Algorithms in Forest Fires Science

Publication typeJournal Article
Publication date2023-07-17
scimago Q2
SJR0.508
CiteScore5.3
Impact factor2.5
ISSN20763417
Computer Science Applications
Process Chemistry and Technology
General Materials Science
Instrumentation
General Engineering
Fluid Flow and Transfer Processes
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.

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