Forest Fire Prediction Research Articles

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.

FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm

Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

Utilizing Deep Learning and Spatial Analysis for Accurate Forest Fire Occurrence Forecasting in the Central Region of China

Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events.

Forest Fire Prediction Based on Time Series Networks and Remote Sensing Images

Protecting forest resources and preventing forest fires are vital for social development and public well-being. However, current research studies on forest fire warning systems often focus on extensive geographic areas like states, counties, and provinces. This approach lacks the precision and detail needed for predicting fires in smaller regions. To address this gap, we propose a Transformer-based time series forecasting model aimed at improving the accuracy of forest fire predictions in smaller areas. Our study focuses on Quanzhou County, Guilin City, Guangxi Province, China. We utilized time series data from 2021 to 2022, along with remote sensing images and ArcGIS technology, to identify various factors influencing forest fires in this region. We established a time series dataset containing twelve influencing factors, each labeled with forest fire occurrences. By integrating these data with the Transformer model, we generated forest fire danger level prediction maps for Quanzhou County. Our model’s performance is compared with other deep learning methods using metrics such as RMSE, and the results reveal that the proposed Transformer model achieves higher accuracy (ACC = 0.903, MAPE = 0.259, MAE = 0.053, RMSE = 0.389). This study demonstrates that the Transformer model effectively takes advantage of spatial background information and the periodicity of forest fire factors, significantly enhancing predictive accuracy.

Toward a more resilient Thailand: Developing a machine learning-powered forest fire warning system

Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation's most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand's forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches.

Hoping the best, expecting the worst: Forecasting forest fire risk in Algeria using fuzzy logic and GIS

Forest fires pose severe threats to ecosystems and communities globally, especially in vulnerable semi-arid regions like North Africa. Understanding the key factors influencing forest fire dynamics is essential for effective management and mitigation. This study aims to comprehensively analyze forest fire risk patterns in Djebel El Ouahch's massif (Algeria), focusing on integrating bioclimatic, fuel, geomorphological, and human factors through advanced fuzzy logic and geographic information system (GIS) techniques. Climatic station data, satellite imagery, and GIS were employed to map bioclimatic parameters, land cover, and geomorphological features. Fuzzy logic systems were applied to integrate these factors, assigning appropriate weights based on their significance. The resulting forest fire prediction model was defuzzified to generate predictive maps indicating varying vulnerability levels within the study area. Predictive maps delineated areas of low to high forest fire risk. Low-risk zones were characterized by sparse vegetation, while high-risk regions featured densely vegetated slopes near human settlements. The study identified critical factors influencing vulnerability, emphasizing the impact of climate, terrain, and human activities. Urgent attention was directed toward high-risk areas, necessitating tailored fire prevention measures and strategic urban planning to minimize human-induced risks. The results underscored the complex interaction of natural and anthropogenic factors in shaping forest fire susceptibility. Understanding these dynamics facilitates evidence-based policymaking, enhancing forest fire preparedness, biodiversity preservation, and community safety. Additionally, the study emphasized the need for continuous research incorporating real-time climate data and socio-economic factors to refine predictive models. This research provided valuable insights into forest fire risk patterns in Djebel El Ouahch, serving as a foundation for targeted fire management strategies. By bridging the gap between theoretical knowledge and practical application, this study contributes significantly to sustainable forest management and disaster mitigation efforts globally, emphasizing the importance of proactive measures in safeguarding vulnerable ecosystems and communities.

Forest Fire Prediction: A Spatial Machine Learning and Neural Network Approach

The study of forest fire prediction holds significant environmental and scientific importance, particularly in regions like South Carolina (SC) with a high incidence rate of forest fires. Despite the limited existing research on forest fires in this area, the application of machine learning and neural network techniques presents an opportunity to enhance forest fire prevention and control efforts. Utilizing data of forest fire from the SC Forestry Commission for the year 2023, prediction models were developed incorporating various factors such as meteorology, terrain, vegetation, and infrastructure—key drivers of forest fires in SC. Feature importance analysis was employed to construct the final fire prediction model using different machine learning and neural network approaches including Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Correlation coefficients analysis was employed to construct the final fire hazard map using a correlation test. The evaluation of predictive performance based on accuracy scores revealed that the DT model achieved the highest accuracy of 90.58%, surpassing other models. However, based on the kernel density map of the fire data from 2000 to 2023, the correlation test gave the better fire hazard map compared to any other machine learning or neural network approach that utilized feature importance. Nonetheless, all models achieved prediction accuracies exceeding 80%. This finding directed us to the approach based on the correlation coefficients rather than to those just based on feature importance. The overlap between fire locations and carbon hotspots provided the immediate need to mitigate the carbon loss due to fire in those locations. These results serve as a valuable resource for forest fire prediction in SC, demonstrating the efficacy of the correlation test, providing a theoretical foundation and data support for future forestry applications in the region, and showing the outperforming capability of this method compared to other approaches based on feature importance and the importance to prioritize areas to mitigate the climate change impact based upon fire prediction.

Understanding the association between global forest fire products and hydrometeorological variables

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

Predictive Modeling of Combustible Water Content of Five Common Tree Species in Central Yunnan Province

The water content of forest combustible material is a key factor affecting forest fire and spread, and is the main reference factor for forest fire prediction and forecasting. The live branches, live leaves, dead branches and dead leaves of cap-dou oak, green oak, Yunnan poplar, Yunnan oil fir and silver wattle in central Yunnan were selected as the research objects, and the slope ( x1 ), slope direction ( x2 ), slope position ( x3 ), elevation ( x4 ), wind speed ( x5 ), wind direction ( x6 ), humidity ( x7 ), ground diameter ( x8 ), crown height ( x9 ), crown width ( x10), tree height (x11), depression (x12), and tree species (x13), and used correlation analysis to screen the main modeling factors, and constructed a prediction model of water content of combustible materials by multiple linear regression method. The results show that the correlation between the four factors of slope (x1), slope direction (x2), wind speed (x5) and humidity (x7) and the water content of combustible materials is significant, the water content decreases when the three influencing factors of slope (x1), slope direction (x2) and wind speed (x5) increase, and the water content is higher when the influencing factor of humidity (x7) increases, and the water content of combustible materials is constructed into a model of water content of 20 groups by these factors. Compare the predicted value of the water content prediction model with the actual value to analyze the error of the model, and the results show that the established model can be used to predict the water content. The errors were caused by rainfall during sampling, which absorbed water into the combustible materials and resulted in errors in the moisture content, and wind speed measurements, which resulted in inaccurate wind speed measurements. The established water content model can provide a basis for the classification of forest danger level and combustibility of combustible materials, and can provide help and reference for forest fire prevention and management.

A Machine Learning Approach to Predict the Occurrence of Forest Fires with Meteorological Parameters

Forest fires have emerged as a considerable challenge in the United States, posing substantial societal, economic, and environmental risks. As a result, the early and accurate prediction of these fires is imperative for management efforts. In this study, we used two Kaggle datasets: the “Algerian Forest Fires Dataset” with fire readings from 2012 and the “Forest Fires Data Set” with readings from 2007. However, because the second data set was originally intended for a regression task, providing approximate area values representing the predicted burned area of the forest fire, we phased the data set out while developing our final model. Ultimately, we used the Algerian Forest Fires Dataset, containing 13 attributes and 244 instances of forest fires in two regions of Algeria. To streamline the analysis, we reduced the number of features to 5, namely, month, temperature, humidity, wind, and rain. Moreover, we developed a Random Forest Classifier model to predict the occurrence of a forest fire, using the data set for training and testing. Performance was compared against Decision Tree, Logistic Regression, and Artificial Neural Network models, using cross-validation. The experiment showed a slight superiority to the Random Forest Classifier approach, achieving an accuracy score of 86.486% and an F1 score of 88.889%. Our approach provides a decimal value representing the probability for fire likelihood. Overarchingly, this research contributes to the advancement of forest fire prediction technologies by leveraging meteorological data.

Correction: Zhang et al. A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data. Forests 2024, 15, 647

There was an error in the original publication [...]

Spatiotemporal dynamics of forest fires in the context of climate change: a review.

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data

Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural habitats and resources. Lightning monitoring data and vegetation moisture content have emerged as pivotal factors among the various influences on lightning-induced fires. This study employed innovative satellite remote sensing technology to swiftly acquire vegetation moisture content data across extensive forested regions. Firstly, the most suitable method to identify the lightning strikes that resulted in fires and two crucial lightning parameters correlated with fire occurrence are confirmed. Secondly, a logistic regression method is proposed for predicting the likelihood of fires triggered by lightning strikes. Finally, the method underwent verification using five years of fire data from the GKM area, resulting in an AUC value of 0.849 and identifying the primary factors contributing to lightning-induced fires in the region.

Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey

Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey

Intelligent System AI as A Service Life on Land

Intelligent System - AI as a service” , is a project to predict the occurrence of forest fire.Forest fires pose a significant threat to both the environment and human safety. Timely prediction of forest fires is crucial for effective mitigation and prevention efforts. To enhance real-time monitoring and early warning capabilities, the system incorporates remote sensing data using machine learning techniques. Fires are now causing an additional 3 million hectares (7.5 million acres) of tree cover loss per year than they did in 2001, according to a newly released Global Forest Watch analysis that examined fires that burn all or most of a forest’s living overstory trees. The majority of all fire-caused tree cover loss in the past 20 years (nearly 70%) occurred in boreal regions. Although fires are naturally occurring there, they are now increasing at an annual rate of 3% and burning with greater frequency and severity and over larger areas than historically recorded. Forest fires pose significant threats to ecosystems, human lives, and property. Early prediction and mitigation of forest fires are crucial to reducing their destructive impact. This abstract presents an overview of a forest fire prediction system that leverages modern technology and data-driven approaches. The proposed forest fire prediction system utilizes a combination of remote sensing data, weather information, historical fire data, and machine learning algorithms to forecast the likelihood of forest fires in specific regions. Advanced techniques such as deep learning and ensemble models are employed to analyze the complex interactions between various environmental factors that contribute to fire ignition and spread. Fires are not naturally occurring in tropical rainforests, but in recent years, as deforestation and climate change have degraded and dried out intact forests, fires have been escaping into standing tropical rainforests. GFW findings suggest fires in the tropics have increased by roughly 5% per year since 2001.

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

AbstractThe escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Forest fires in Türkiye have devastated 2.5 million hectares of habitat over four decades, posing a grave threat to Mediterranean forest ecosystems. This study compares machine learning techniques: Decision Trees (DT), Naive Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), for predicting forest fire vulnerability. Using a dataset encompassing various factors like precipitation, soil moisture, temperature, humidity, wind speed, land cover, elevation, aspect, slope, proximity to roads/electricity networks, and population density, the models were trained and tested. The dataset classified vulnerability into four classes: very low, low, moderate, and high. Evaluation metrics included overall accuracy, precision, sensitivity, F1-score, Cohen kappa, and cross-validation (CV).RF exhibited the highest performance (accuracy: 0.80, precision: 0.78, sensitivity: 0.80, F1-score: 0.78, Cohen kappa: 0.71, average CV: 0.71), predicting fire vulnerability classes very low (14.99%), low (0.68%), moderate (65.41%), and high (18.90%) with notable accuracy. DT yielded consistent results, while NB performed stably, though slightly lower than RF and DT. However, ANN and SVM demonstrated lower performance and higher variability. These findings advocate for RF as the most accurate algorithm for forest fire risk prediction, emphasizing its crucial role in proactive fire risk management strategies.

A Neural Network Approach to Forest Fire Burned Area Prediction

Worldwide ecosystems and economies are seriously threatened by forest fires, which can result in extensive damage and financial losses. Strategies for managing and preventing wildfires depend heavily on the accurate prediction of burned areas. To create a predictive model for the burned areas caused by forest fires in Portugal's northeast, this study uses a machine learning technique, namely Neural Network. The model integrates meteorological and spatial-temporal data and makes use of regression algorithms. To maximize the performance of the model, data preprocessing, exploratory data analysis, model building, feature selection, and assessment are done. The results show that burned area is greatly influenced by meteorological factors, including temperature, humidity, and wind speed. The created model shows a respectable level of prediction accuracy of 97.11%, offering insightful information for efficient fire management and early warning systems. The potential for reducing the destructive effects of wildfires is enormous when machine learning is incorporated into the prediction of forest fires.

Cellular Automata on GIS Method for Forest Fire Spreading Simulation

A method for prediction and simulation based on the Cell Based Geographic Information System (CBGIS) as Cellular Automata (CA) is proposed together with required data system systems, in particular metasearch engine usage in a unified way. It is confirmed that the proposed CBGIS as CA has flexible usage of the attribute information that is attached to the cell in concern with location information and does work for disaster of forest fire spreading simulation and prediction.

Orman yangınları çalışmalarında uygulanan makine öğrenmesi (ML) tekniklerine genel bir bakış

With the increasing frequency of forest fires globally, causing substantial environmental and economic damages, there is an imperative need for early fire prediction and detection. This study aims to examine the utility of machine learning techniques in predicting and identifying forest fires. A comprehensive review was conducted on various technologies and techniques proposed for forest fire prediction. Particular emphasis was placed on understanding the pros and cons of each machine learning algorithm, with an aim to identify the most effective approaches. It was observed that while numerous machine learning methods exist for forecasting forest fires, each possesses unique strengths and limitations. Some techniques, when tailored to specific forest characteristics, displayed enhanced predictive capabilities. Machine learning (ML) plays a pivotal role in advancing the field of forest fire studies. Identifying and utilizing the most suited ML technique, based on forest characteristics and the nature of data, can significantly augment prediction accuracy.