# A Brief Review of Machine Learning Algorithms in Forest Fires Science
###### 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. machine learning; forest fires; wildfire; deep learning; drone; UAV; remote sensing; Google Earth Engine (GEE) +
Footnote †: journal: _Applied Sciences_
Although fire activity can be measured on various scales (centimeters to kilometers, seconds to millennia), it does face some limitations. For instance, combustion and fire formerly are physicochemical processes that may be effectively signified at relatively fine scales in mechanistic models [4]. Nevertheless, the capacity to resolve important physical processes and availability of input data and the quality frequently constrain such models [5]. Furthermore, due to restraints associated with present processing capacity, it is impossible to use physical models to influence research and fire management at bigger and longer scales that are occasionally required in near real-time. Thus, forest fire management and science strongly rely on creating empirical and statistical models. For meso-, synoptic-, strategic-, and global-scale phenomena [6], the value of which is contingent on their capacity to capture the frequently complicated and nonlinear interactions between variables of interest, in addition to data availability, including data quality. Although the intricacies of forest fires sometimes create modeling issues, significant breakthroughs in forest fire observation and monitoring have been accomplished, mainly due to the capabilities and increased availability of remote-sensing technology. Several satellites (NASA, TERRA, and AQUA) contain onboard fire detection sensors (Advanced Very High-Resolution, Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS)). These sensors regularly monitor changes and vegetation distributions. Furthermore, advances in numerical weather prediction and climate models provide lesser geographical resolutions and lengthier lead forecast times [7], potentially improving forecasting of intense fire weather occurrences. Given enough data, such advancements make a data-centric approach to forest fire modeling a natural progression for many research challenges. As a result, there has been a surge in attention to applying machine learning methods in forest fire management and science in current years. Despite the lack of a formal definition, we embrace the ordinary meaning of ML as the study of computer algorithms that can improve themselves spontaneously via experience [8]. This method is inherently data-centric, with ML algorithm success determined by the quality and amount of accessible data relevant to the job. In recent years, ML has grown rapidly in the context of data analysis and computing, which typically allows the applications to function in an intelligent manner. AI researchers seek to comprehend and synthesize intelligent beings capable of acting in accordance with their circumstances and aims, adapting to changing surroundings, and learning from experience [9].