# A Brief Review of Machine Learning Algorithms in Forest Fires Science
## 3 Review of ML Technologies and Their Applications in Forest Fire Science
### Fire Mapping  
ML methods have been relatively recently introduced in fire mapping studies in comparison to other fields. However, these studies have already incorporated a diverse range of ML methods. Below are some of the methods recommended by research studies:  
Ref. [76] used UAV imagery as research data to map fire severity. Their experiment employed the Random Forest Classifier, which achieved a performance accuracy of 89%.  
Using the logistic regression method and GIS-based data, ref. [77] carried out a study to map the fire ignition risk based on human activities and presence factors. Their research yielded a global accuracy of 79.8%, an ignition prediction accuracy of 78.2%, and an ignition prediction accuracy of 82.7%.  
In their study, ref. [78] used the SVM method to analyze meteorological data in order to identify burned areas that they had reached. The MAD and RMES predictions were 13.07 and 64.7, respectively. Their study's performance has a 12.7% error rate.  
Ref. [79] did a study with the goal of creating a map of the susceptibility of forests to fires. To do this, they used BPN to evaluate GIS-based data. Their research produced a 78% agreement.  
On the study site, ref. [80] assessed fire severity mapping using Sentinel satellite imagery and a Random Forest classifier. Their study performed admirably, with a 98% accuracy level.  
Ref. [81] mapped the burned areas using Landsat Thematic Mapper imagery (TM). With a performance accuracy of more than 93%, they have identified it using SVM and nine additional classifiers.  
Ref. [82] used spectral bands (MODIS) as data assessed by the supervised minimum distance classifier to map the burned areas with 90% accuracy.  
Ref. [83] investigated the spatial performance of forest fires by evaluating the weather, vegetation, and infrastructure using the MARS-DFP classifier. The study was completed with an accuracy of 86.5%.  
In a study by [84], five hybrid machine learning algorithms were developed to map forest fire susceptibility in the northern region of Morocco. These algorithms are Frequency Ratio-Multilayer Perceptron, Frequency Ratio-Logistic Regression, Frequency Ratio-Classification and Regression Tree, Frequency Ratio-Support Vector Machine, and Frequency Ratio-Random Forest. The mapping process involved utilizing a dataset consisting of 510 historic forest fire points as the forest fire inventory map and ten independent causal factors, including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index, rainfall, temperature, and wind speed. To evaluate the models' effectiveness, the area under the receiver operating characteristics curves (AUC) was computed. The results revealed that the Frequency Ratio-Random Forest model achieved the highest performance with an AUC of 0.989, followed by Frequency Ratio-Support Vector Machine with an AUC of 0.959, Frequency Ratio-Multilayer Perceptron with an AUC of 0.858, Frequency Ratio-Classification and Regression Tree with an AUC of 0.847, and Frequency Ratio-Logistic Regression with an AUC of 0.809 in predicting forest fire occurrences.