# A Brief Review of Machine Learning Algorithms in Forest Fires Science
## 3 Review of ML Technologies and Their Applications in Forest Fire Science
### Fire Detection  
There have been several studies aimed at early fire detection in forest fires since fire detection is an important area. There is growing interest in the use of newer technologies in fire detection, such as machine and deep learning. Here are some methods that have been proposed by research works. In order to design a false alarm reduction system, ref. [34] conducted a study to evaluate multi-source data, such as metrological geographic information, as well as visual and infrared camera data. A detection rate of over 98% and a false alarm rate of 1.93% were achieved by the study, which used WSN approaches in conjunction with the Backpropagation Network (BPN), Radial Base Function Network (RBFN), Dynamic Learning Vector Quantization (DLVQ), and MLP. By assessing the Canadian FWI system's forest fire features and residential fire temperature Ionization Photoelectric CO gas, ref. [35] performed data analysis for the detection of forest fires and residential fires. Distributed ANN and Naive Bayes were both used to analyze these data. The accuracy of home fire detection in their study was 81%, whereas the accuracy of forest fire detection was 92%. Ref. [36] used UAV-based aerial images as their data to detect forest fires using a Convolution neural network (CNN). Their study had an 83% preference accuracy. The study's goal was to self-organize and fault-tolerate the WSN model for forest fire detection, which they achieved by evaluating meteorological data using the DT method [37]. Their study failed detection of about 45% of possible failure identification in the application. Ref. [38] evaluated 17 fire films using FLURIA, OneR, and NN as classifiers in a study to detect video-based fire. Their research produced an SVM of 90.9%. Ref. [39] conducted a study similar to [36], in which they used the same classifier to detect forest fires. However, they switched to aerial 360-degree imagery, which resulted in higher performance accuracy (94%). Ref. [40] study aimed to detect forest fires using UAV by analyzing thermal camera data using the Multilayer NN method. Their research findings are not available. Ref. [41] used MLP with WSN as classifiers to analyze a dataset made up of relative temperature, humidity, smoke, and wind speed in order to detect forest fires in real-time. An average communication load ratio of 2.5% to 8% was found in their investigation, both with and without the use of the NN technique. Using a fuzzy unordered rule induction algorithm (FURIA), NN, and OneR, ref. [42] conducted a study comparing data mining approaches on WSN-based fire detection systems. They arrived at their conclusions by assessing various combinations of temperature, humidity, light, and CO. Three percentages of occurrences were accurately classified as a consequence of their study: FURIA: 87.6, OneR: 71.6, and NN: 93.8%. In order to identify fire using a data fusion system, ref. [43] did a comparison study. To do this, they evaluated temperature, humidity, light intensity, and CO using fuzzy logic in the WSN approach. They did not have any outcomes for their study. Ref. [44] looked into a CNN camera-based fire detection model that has been fine-tuned through the analysis of CCTV security cameras. Their investigation's accuracy, precision, recall, and F-measure were 94.3%, 0.82, 0.98, and 0.89, respectively. According to [45], the study aimed to detect forest fires by evaluating fire and fire-like object videos using the Rule-Based image processing algorithm. For their study, recall was 93.13%, precision was 92.59, F-score was 92.86, and the false detection rate was less than 40%. Ref. [46] study's goal was to detect fires with fuzzy logic and a home monitoring system, which they achieved by evaluating temperature, humidity, CO, and smoke in the WSN method with fuzzy logic. Their study yielded a 6.67% error ratio. The study's goal was to detect forest fires using multi-sensor WSN, which they accomplished by evaluating temperature, humidity, smoke, and light sensors using the Naive Bayes method [47].