# 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  
Disaster preparedness depends on predicting forest fires. Novel forest fire prediction technology would, therefore, enable better management of forest fires. This domain has been successfully tackled through the use of machine learning and deep learning methods. Some methods suggested by research works are listed below. According to [PERSON] [51], the study evaluated topology, infrastructure, and socioeconomic data before using the RF predictor to forecast fire. Their research produced an accuracy rate of 78%. The chance of a fire occurring was explored by [52] using a Bayesian analysis of data based on eyesight. A false positive rate of 0.68% and a false negative rate of 0.028% were found in their study. In order to forecast fire ignition, ref. [53] used logistic regression to analyze terrain, vegetation kinds, meteorological conditions, climate, and human activities. Their research produced an accuracy rate of 85.7%. Ref. [54] used MLP and fuzzy logic techniques to analyze multi-sensor data, temperature, smoke density, and CO density in order to predict fire likelihood. The error value from their study was \(10^{-4}\). The ignition probability prediction of a forest fire was made using a model created by analyzing data from raster GIS, including the date of occurrence, geographic coordinates, cause of ignition, land use, and burned areas [55]. These data were evaluated using logistic regression and MLP as experimental techniques. Their findings included ANN accuracy of 75.5% with ignition and 87.8% without ignition, as well as logistic regression accuracy of 78.8% with ignition and 74% without ignition. Research on chosen and prioritized biotic, abiotic, and human factors that affect forest fire activity was done by [56]. They used the BNN approach to analyze a satellite-based fire dataset (MODIS) for their investigation. A recall of 0.963, a specificity of 072, and an accuracy of 0.961 were obtained from their study. Ref. [57] used the CNN method to evaluate landscaping, fuel types, and weather conditions in order to predict the time-resolved spatial evolution of a forest fire. Their research produced a mean precision of 97%, sensitivity of 925, and an F-measure of 93%. Ref. [58] used the LSTM classifier to evaluate weather and forest fire data to predict the forest fire scale, which performed with 90.9% accuracy. Ref. [59] found that using weather, location, and time to predict forest fires using a fuzzy inference system was 75% accurate. By employing the CART technique to assess environmental characteristics such vegetation status, accessibility, fire history, and topography, ref. [60] sought to identify the occurrence of fire in certain models. The accuracy of their study was 88.39%. In their study, ref. [61] evaluated the NDVI composite MODIS data using the Multilayer feedforward networks (MLFN) as the classifier to determine the high-risk FFD based on information in the pixels of multi-temporal satellite pictures. The accuracy and MSE values of their investigation were 90% and 0.07, respectively. Ref. [62] used Random Forest (RF) Multiple Linear Regression to evaluate 37 variables taken from multiple databases spanning diverse features in order to develop models of fire occurrence likelihood and the contributing factors (MLR). According to the RF classifier, their investigation produced a total price-fire season variable of 93.31% and 179% for no-fire season. The MLR processor's metrics for the no-fire season variable and the fire season variable were, respectively, 49.193 and 22.15. In order to study human-caused fire occurrence modeling, ref. [63] employed logistic regression to assess weather information, regional traits, and historical records of daily fire. Their research produced an accurate range for the total proportion of accurately anticipated fires of 47.4% to 82.6%. Ref. [64] used the Boosted regression tree classifier to mask a forest fire's susceptibility. They assessed using two satellite images (OLI and MODIS), yielding an accuracy of 0.89. Ref.