# A Brief Review of Machine Learning Algorithms in Forest Fires Science
## 3 Review of ML Technologies and Their Applications in Forest Fire Science
### Evaluating Data Collected from Forest Fires  
Datasets have played a pivotal role in driving progress and fostering innovation in machine learning research. They enable the evaluation and comparison of model performance. As the availability of extensive forest fire datasets continues to increase, there is a significant opportunity to utilize ML and DL methods that can efficiently extract relevant features from the data. Here are some studies covering several datasets and ML methods in forest fire domain.  
The CNN method was used in a study by [85] to evaluate the IRIS dataset. There were no reported outcomes.  
A study by [86] employed the R-CNN method to evaluate data from the ConFoBi project. Their analysis produced a precision of 43.4% and a precision of 92.4%.  
The CNN method was used in a study by [87] to evaluate images captured by a drone. There were no published findings from the study.  
The YOLOv3 method was used in a study by [88] to evaluate drone videos. Their study had an 82% precision, 79% recall, and a FI score of 81%.  
In a study by [89], the Bi-CNN method was used to evaluate the YUPENN, BUAA, and Maryland datasets. Their research yielded a mean accuracy of 93%.  
A study by [90] compared the UAV dataset Kaggle, drone images, and open-source photos using the DenseNet121, Resnet52, and MobilNetv2 networks. Their research yielded a DenseNet accuracy of 93.1%.  
A study by [91] used DenseNet121, Resnet52, and MobilNetv2 to evaluate COCO-Dataset. Their research yielded a mobile net accuracy of 87.5%.  
Ref. [92] evaluated images created with the CycleGan and DenseNet methods. The study's results were 99.38% precision, 98.16% FI-Score, and 98.27% accuracy.  
A study by [93] used CNN and RNBFE methods to evaluate the UCM and WHU RS datasets. Their research yielded UCM accuracy of 97.84% and WHU accuracy of 97%.  
The FLAME dataset was evaluated using the CNN and UNet methods in a study by [94]. Their research yielded CLA accuracy of 76.23%, SEG recall of 83.88%, precision of 91.99%, and a FI score of 87.75%.  
A study by [95] used UNet++ and UNet methods to evaluate data collected from a forest fire in Andong, Republic of Korea, on April 20. Their study yielded specificities of 91.77% and 83.11%.  
A study by [96] used mobile net V3 and YOLOv4 methods to evaluate MSCOCO and collect images. Their research yielded 99.21% recall, 99.21% precession, 99.57% accuracy, and 75.68% interference time reduction.  
A study by [97] used Mobile Net v2, CNN, FireNet, and AlexNet methods to evaluate 2096 images collected from the internet. The study yielded 2.5M parameters with a 99.3% accuracy.  
A study by [98] employed FireNet to evaluate images from Google on Baida and the AlnML lab dataset (DCNN). Their study had a 98% accuracy rate.  
A study by [99] evaluated drone images using the author's model as the method. Their research yielded an accuracy of 81.97%.  
In a study by [100], seven ML approaches were employed to assess active fire pixels obtained from MODIS monthly MCD14 ML composites. The ML approaches utilized were Logistic Regression, SVM, Linear Discriminant Analysis, as well as ensemble algorithms, such as eXtreme Gradient Boosting, Random Forest, Gradient Boosting, and AdaBoost (AB). Five performance metrics, namely average accuracy, F1 score, precision, recall, and area under the curve, were employed for evaluation. The AUC values ranged from 0.817 to 0.879 across the seven methods, while accuracy scores ranged between 0.734 and 0.812. The results indicated that the RF model consistently outperformed the other approaches across all performance metrics.