# A Brief Review of Machine Learning Algorithms in Forest Fires Science
## 3 Review of ML Technologies and Their Applications in Forest Fire Science
### Fire Prediction
[65] investigate the establishment of forest fire susceptibility by evaluating topographical, meteorological, and geological data using BRT, GAM, and RF methods. Their research yielded three inaccuracies (BRT: 80, 74; RF: 72, 79; GAM: 87, 70). Ref. [66] investigated the occurrence of fire forecasting by analyzing weather data with the auto-learn framework, achieving an accuracy of 87%. Using three classifiers, ref. [67] evaluated the following data: Socioeconomic, economic activity, and fire-causing potential (LR, SVM, RF). Their research had a 74.6% performance accuracy and was intended to investigate the frequency of human-caused forest fires. A study by [68] uses several machine learning models to predict the spread and behavior of forest fires using a dataset collected from Brazilian government open data. Their research yielded the following findings: AdaBoost model accuracy is 91%, RF model accuracy is 88%, ANN model accuracy is 86%, and SVM model accuracy is 81%. Weather observations were used by [69] to forecast the Fire weather index. The NN predictor was utilized, and the performance error rate was 9%. Ref. [70] conducted a study to predict the occurrence and size of forest fires, which they accomplished by analyzing a monthly resolution global dataset of burned areas in 0.25\({}^{\circ}\) 0.25\({}^{\circ}\) regions around the entire globe for the year 2015 using multiple ML models such as random forest, XGBoost, multilayer perceptron, and logistic and linear regressions. Their study yielded an XGBoost accuracy of 94%, logistic regression accuracy of 81%, RF accuracy of 89%, and MLP accuracy of 90%. By analyzing GIS data, multi-temporal MODIS data, and meteorological ALADIN data using the BT, RF, Logistic regression, Naive Bayesian (NB), and SVM algorithms, ref. [71] were able to predict forest fires. The outcomes of their research were as follows: 84.9% accuracy for BT, 82.5% accuracy for RF, 83% accuracy for logistic regression, 81% accuracy for NB, and 83% accuracy for SVM. Ref. [72] predicted the danger of forest fires using the same data as [59]. They did, however, use a different predictor, the DFP-MnBpAnn. A study by [72] has a higher performance accuracy of 89%. In a study on predicting the factors' significance in a fire initiation threat scheme, ref. [73] used MLP and BPA to analyze meteorological data, vegetation, and topological human presence data. According to their research, the following factors (PI) had the highest percentages of influence: 35.9% rainfall in the previous 24 h, 1028.7% temperature, 60.3% fuel moisture (10 h), 16.9% aspect, 17.3% primary road network, and 14.3% for the month of the year. Ref. [74] conducted a comparative study of ANN and logistic regression models in the context of human-caused fire prediction systems. They evaluated spatially differentiated data managed in a Geographical Information System and applied logistic regression (RBFN) as a method. Their study yielded an ANN accuracy of 85% for no-fire correct prediction. In a study by [75], a combination of locally weighted learning (LWL) algorithm with ensemble learning techniques, such as Cascade Generalization (CG), Bagging, Decorate, and Dagging, was employed to predict forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. The training process involved utilizing a geospatial database containing 56 historical fire records and nine explanatory variables to train the standalone LWL model as well as its derived ensemble models. To assess the models' performance and predictive capability, several statistical performance criteria were employed, including the area under the receiver operating characteristic curve (AUC). The CG-LWL and Bagging-LWL models exhibited the highest training performance, with an AUC of 0.993. Furthermore, the Dagging-LWL ensemble model, with an AUC of 0.983, outperformed the Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and standalone LWL (AUC = 0.965) models in predicting the spatial pattern of fire susceptibilities across the study area.