# A Deep-Learning-Based Approach to the Classification of Fire Types
## 2 Related Work
The authors highlighted the most common machine-learning and deep-learning models primarily used for wildfire issues, such as random forests, decision trees, neural networks, support vector machines, K-nearest neighbours, Bayes networks, naive Bayes,decision trees, and multivariate logistic regression. In recent years, they noticed that the artificial neural network (ANN) is the most commonly used fire detection algorithm. This aligns with the work of [16], wherein the authors utilized a machine-learning model for fire prediction in the coal storage industry. The researchers used artificial neural networks and convolutional neural networks for fire prediction. The work of [17] proposed a system to detect early fire symptoms in smart homes using various sensors and fuzzy logic. They used sensors and real-time data to detect fire symptoms in smart houses. In [18], the authors investigated the use of different fire sensors to enhance early fire detection in underground diesel-fuel storage areas, focusing on improving safety in underground mines. Diesel-fuel fire tests were conducted in a simulated underground storage area at the NIOSH Safety Research Coal Mine. The study concluded that smoke and flame sensors provide more rapid fire detection than CO sensors, with flame sensors showing the best overall performance. The findings suggested optimal sensor placements to enhance early fire detection and ensure the safety of underground miners. The work of [19] explored integrating machine learning with mechanistic models to improve fire engineering and sciences (FESs). The paper advocated for using ML to address complex problems in FESs by showcasing their potential through various examples and recommended procedures. They used a dataset compiled from historical fire tests, sensor data, and simulation outputs. The paper concluded that leveraging machine learning and artificial intelligence techniques, mainly mechanical models, can significantly enhance fire engineering's and fire sciences' predictive capabilities and efficiency. Similarly, [11] compared several image-processing-based fire-detection methods using rule-based and machine-learning (ML) approaches. The objective was to improve the accuracy and reliability of fire detection in wildland areas under various conditions. They combined the rules with a dataset of 500 outdoor images and logistic regression. The study concluded that the proposed logistic regression method, which incorporates rules using machine learning, provides the best performance for fire pixel detection. It demonstrated the potential for developing more robust fire detection techniques suitable for unstructured environments, addressing the limitations of individual rule-based methods. In [20], the authors focused on energy applications wherein the type of fuel used necessitates specific chemical processes for optimal results. Misprocessing fuels can lead to waste and inaccurate conclusions, particularly when the material has already been processed, complicating classification. The authors employed a machine-learning approach to classifying fuels using proximate analysis results, which included fixed carbon, volatile matter, and ash content. Data were collected from the literature and classified into four categories: coals, woods, agricultural residue, and manufactured biomass. Three machine learning classifiers--K-nearest neighbour, support vector machines, and random forest--were used to develop prediction models. A hierarchical classification approach was implemented. The classifiers' performances were evaluated using K-fold cross-validation, achieving 96% accuracy in the training phase and 92% in the testing phase. The study confirmed that machine learning, combined with proximate analysis, is a viable approach to fuel classification, offering a more systematic and reliable alternative to traditional methods. In more recent work by [21], the authors discussed the development of a computational framework to model and simulate aerial drops of fire retardants in dangerous fire environments.