Forest Fire Susceptibility Research Articles

Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches

Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.

Artificial neural networks for assessing forest fire susceptibility in Türkiye

Wildfires often threaten natural and economic resources and human lives. Wildfire susceptibility assessments have become essential for efficient disaster management and increasing resilience. In this study, we assessed the forest fire susceptibility in Istanbul Province and Thrace Region, Türkiye using a well-known machine learning technique, Artificial Neural Networks (ANN). Benefiting from freely available Earth Observation datasets such as Sentinel-2 images, Tree Cover Density from European Union (EU) European Environment Agency (EEA) Copernicus Land Monitoring Service, Shuttle Radar Topography Mission (SRTM) data, etc., and a forest inventory with ignition locations recorded over a period of eight years, we utilized a total of 16 independent and one dependent variables. The variables can be categorized as anthropogenic, topographic, vegetation, and hydrological factors. A ratio of 1:2 was preferred for the fire/non-fire location samples. The results show that the ANN exhibited high prediction performance with Area Under the Receiver Operating Characteristic Curve (AUC) value and F-1 score of 0.94 and 0.80, respectively. Based on feature importance analyses, we found that a human-related factor, proximity to forest roads, was the most predictive input variable. The ANN model trained with openly available data (i.e., without forest database) also yielded a high F-1 score, but produced maps with fewer details. Our results confirm that data-driven machine learning methods are promising for regional forest fire susceptibility assessments and can be extended further for other regions by deriving similar parameters from freely available Earth Observation datasets.

USING MACHINE LEARNING COUPLED WITH REMOTE SENSING FOR FOREST FIRE SUSCEPTIBILITY MAPPING. CASE STUDY TETOUAN PROVINCE, NORTHERN MOROCCO

Abstract. Currently there is a public awareness to protect the environment especially forest ecosystems and the forest fire dilemma has become a topic of intense research around the world. In this setting, this study evaluates forest fire susceptibility (FFS) in northern Morocco using three geographic information system (GIS) based on machine learning algorithms: XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). To this effect, a Geographic Information System (GIS) database was developed involving ten independent causal factors (elevation, aspect, slope, distance to roads, distance to residential areas, land cover, normalized difference vegetation index (NDVI), rainfall, temperature and wind speed) and 345 fire pixels. The 345 pixels were split into two sets for training (70%) and validation (30%) and the spatial relationships between factors affecting FFs and fire pixels was analyzed using XGB, RF, and SVM models to generate the FFS maps. The effectiveness of the models was evaluated using the receiver operating characteristic curve, the area under the curve (AUC), and several statistical measures. The results of the three models hinted that XGBoost had the highest performance (AUC = 0.856), followed by RF (AUC) = 0.827), and SVM (AUC = 0.803) in the forecasting of the forest fire. The resulting FFS maps areas can provide crucial support for the management of Mediterranean forest ecosystems and can enhance the effectiveness of planning and management of forest resources and ecological balances in these areas.

Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources

Periodic forest fires occur in sub-tropical deciduous forest of Ayodhya hill region which is extended part of Eastern Ghats Mountain Range (India). This forest provides diverse ecosystem services (mostly non timber forest products)to tribal and non-tribal people throughout the year. The main objective of this study is to demarcate the accurate forest fire susceptibility zones applying three relevant machine learning models i.e., Random Forest (RF), Multivariate Adaptive Regression Splines (MARS) and Deep Learning Neural Network (DLNN). In this research, more than 300 hundreds historical forest fire events along with 14 forest fire predictors have been considered. DLNN model has the highest efficacy (AUC = 0.925). Features of DLNN are instinctively presumed and optimally tuned for required outcomes. Applied models explicitly examined that the north western and eastern segments of Ayodhya hill are highly susceptible for forest fire due to its physical and social complexity. DLNN model investigates that total 18% areas of Ayodhya hill have come under very high susceptible zone. The forest fire incidents are taken place during March to May because of the effect of high temperature, poor moisture content and existence of high fuel woods. Both the machine and deep learning techniques have been applied first time in this dry deciduous forest belt and models indicated high accuracy and precision which is harmonized with ground reality. This study will definitely help the local government to take proper management and conservation strategies for sustainable forest resources conservation along with improvement of livelihood of forest and forest fringe dwellers.

GIS analysis of the forest fire occurrence possibilities and their impact on tourism offer of the Sokobanja municipality

The tourism sector, which is a significant driver of economic development, is particularly vulnerable to the risks of forest fires. Forest fires are a natural disaster that can have enormous consequences for tourism. This paper analyses the possibility of forest fires occurrence and their impact on tourist facilities in the Sokobanja municipality. Forest fires can occur naturally or as a result of anthropogenic impact. For the purposes of this analysis data about land cover, relief characteristics (slope and aspect) and distance from roads and settlements were used and processed in GIS environment (open source software ?QGIS?). Based on the forest fire susceptibility index, areas with different degrees of endangerment were identified, which were then overlapped with the locations of tourist facilities. The applied methodology can be used except for the identification of endangered areas and the prediction of the degree of danger to the tourist offer and for the planning of appropriate prevention measures.

Modeling and prediction of fire occurrences along an elevational gradient in Western Himalayas

Forest fires are the result of complex interactions among human, geographic and weather conditions. Climate change would alter the link between forest fire and the controlling factors. The objective of the study is to model the forest fire occurrences and quantify the contribution of explanatory geographic, climatic and anthropogenic variables using satellite-derived historical fire data (2003–2019) and machine learning classifiers over the western Himalaya, India. The climatic variables were derived from a regional Earth system model (ROM). Along with the key selected explanatory variables, the conditions of neighbouring (3 × 3) pixels were incorporated to account for the contribution from the surrounding area. Out of the selected classifiers, random forest recorded the most promising performance in k-fold cross-validation (f2-score = 0.95 and f1-score = 0.94) as well as in the final model validation (f2-score = 0.85 and f1-score = 0.84). The elevation and mean neighbour elevation exhibited the highest influence (8.18% and 6.72%, respectively) in forest fire occurrences followed by near-surface temperatures (4.65–5.78%). We predicted the forest fire susceptibility [0, 1] for 2030, 2040 and 2050 using the future climate projections. The predicted map can be useful to plan effective fire management strategies to minimize damage to the forest ecosystem.

Assessing the impact of ecological security and forest fire susceptibility on carbon stocks in Bo Trach district, Quang Binh province, Vietnam

An assessment index of landscape ecological security (LES), different from other ecological models namely Pressure-State-Response (PSR), will be more accurate to capture temporal and geographic changes in landscape as well as ecosystem resilience and resistance to interference. In the present study, the impact of ecological security and forest fires on the carbon stock of forests in Bo Trach district, Quang Binh province, central Vietnam was evaluated and analyzed based on remote sensing data and a hybrid model of ant colony optimization (ACO) and neuro-fuzzy system (NFS). The present study indicated that forest fires are generally high throughout the study area, concentrating on areas exposed to and affected by human reclamation and production activities. The artificial neural network (ANN) model based on principal component analysis (PCA) combined Sentinel-1A data performed a higher prediction accuracy (R2 = 0.74), being much greater than biomass estimation using optical data. It reveals that there is a reliability in estimating the aboveground carbon stocks (AGCs) from the aboveground biomass (AGB). The calculated data suggest the AGCs in the study area is high, but these parameters will loss severely in the coming years due to the nature and humans impacts. These results show that utilizing remote sensing combined with PCA-ANN model would have increased the accuracy of forest fire susceptibility, and detected assessment of the LES and forest fires on the AGCs. The above obtainings supply helpful information for managers and forest rangers to guard the forests in the study area better, and to limit human encroachment, thereby offering actions that contribute to sustainable development.

Forecasting wildfires in major forest types of India

Severity of wildfires witnessed in different parts of the world in the recent times has posed a significant challenge to fire control authorities. Even when the different fire early warning systems have been developed to provide the quickest warnings about the possible wildfire location, severity, and danger, often it is difficult to deploy the resources quickly to contain the wildfire at a short notice. Response time is further delayed when the terrain is complex. Early warning systems based on physics-based models, such as WRF-FIRE/SFIRE, are computationally intensive and require high performance computing resources and significant data related to fuel properties and climate to generate forecasts at short intervals of time (i.e., hourly basis). It is therefore that when the objective is to develop monthly and yearly forecasts, time series models seem to be useful as they require lesser computation power and limited data (as compared to physics-based models). Long duration forecasts are useful in preparing an efficient fire management plan for optimal deployment of resources in the event of forest fire. The present research is aimed at forecasting the number of fires in different forest types of India on a monthly basis using “Autoregressive Integrated Moving Average” time series models (both univariate and with regressors) at 25 km × 25 km spatial resolution (grid) and developing the fire susceptibility maps using Geographical Information System. The performance of models was validated based on the autocorrelation function (ACF), partial ACF, cumulative periodogram, and Portmanteau (L-Jung Box) test. Both the univariate- and regressor-based models performed equally well; however, the univariate model was preferred due to parsimony. The R software package was used to run and test the model. The forecasted active fire counts were tested against the original 3 years monthly forecasts from 2015 to 2017. The variation in coefficient of determination from 0.94 (for year 1 forecast) to 0.64 (when all the 3-year forecasts were considered together) was observed for tropical dry deciduous forests. These values varied from 0.98 to 0.89 for tropical moist deciduous forest and from 0.97 to 0.88 for the tropical evergreen forests. The forecasted active fire counts were used to estimate the future forest fire frequency ratio, which has been used as an indicator of forest fire susceptibility.

Fine-Tuning LightGBM Using an Artificial Ecosystem-Based Optimizer for Forest Fire Analysis

Abstract This study’s main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm named LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. Four hundred twenty-six historical forest fires from the NASA portal and thirteen conditional factors including elevation, aspect, slope, curvature, normalized difference vegetation index, normalized difference water index, distance to residence, distance to road, distance to river, temperature, rain, humidity, and wind were used to train the model. The model performance was evaluated and compared with other benchmark models using root mean square error, area under receiver operating characteristics (AUC), and overall accuracy. The results show that the proposed model (AUC = 0.9705) outperforms others, such as Random Forest (AUC = 0.958), AdaBoost (AUC = 0.905), Bagging (AUC = 0.945), and Random Subspace (AUC = 0.938), respectively. The final model was interpreted to better understand the most influential factors of forest fire hazards.

Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms

The frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring and prevention of forest fires. This study proposed a method to establish a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multi-layer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas. First, extreme drought years were selected using the standardized precipitation index (SPI). An inventory drought map was constructed using the enhanced vegetation index (EVI) based on satellite image data. Then, 70% of the inventory maps based on forest fires and droughts were used to construct hazard susceptibility maps and 30% of these were used to validate three machine learning models: Classification and Regression Trees (CART), Random Forest (RF) and boosted Regression Trees (BRT). Eleven conditioning factors related to climate, topography, hydrology, and human activities were considered for the analysis. The results of the three models were then validated using the area under the receiver operating characteristic (ROC) curve (AUC), and the best performing model was selected (BRT; forest fire: 85%, drought: 80%). Finally, the susceptibility maps of forest fires and droughts were combined to construct the MHPM for the Gangwon Province, South Korea. The results show that the MHPM of forest fires and droughts constructed in this study is valid and reliable. This multi-hazard map can provide key information for planners and decision-makers to develop forest fire prevention and management plans and to more effectively prevent and reduce the frequency of forest fires.

A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.

Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms

Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been developed using a combination of either the SVR or ANFIS and evolutionary algorithms, but there are only a handful of studies that compare the performance of these models when integrated with evolutionary algorithms, especially in forest fire susceptibility mapping (FFSM). The aim of this study was to compare performance of ANFIS-, and SVR-based evolutionary algorithms, namely, the genetic algorithm (GA) and the shuffled frog-leaping algorithm (SFLA) in FFSM in Ajloun Governorate in Jordan. Accordingly, four hybrid models, SVR-GA, SVR-SFLA, ANFIS-GA, and ANFIS-SFLA, were developed and compared. One hundred and one forest fire locations were used in this study to assess and model susceptibility of forests to fires. The forest fire inventory data were divided into a training data subset (70%) and a testing data subset (30%). Fourteen factors affecting incidence of forest fires were employed as conditioning factors. The area under the receiver operating characteristic (AUROC) curve was used to assess performance of the models in the validation phase. The results revealed that the SVR-based hybrid algorithms had better AUROC values than the ANFIS-based algorithms. Of the four integrated models, the SVR-GA model proved to be the model with the highest accuracy and best performance. It had AUROC values of 0.97 and 0.89 in the training and the testing phases, respectively.

GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria

Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

Assessment of Three Machine Learning Techniques with Open-Access Geographic Data for Forest Fire Susceptibility Monitoring—Evidence from Southern Ecuador

Forest fires have become a habitual threat in all types of ecosystems, which is the reason why it is necessary to improve management of the territories and optimization of prevention and means of extinction. This study compares three machine learning techniques: logistic regression, logistic decision tree, and multivariate adaptive regression spline to identify areas susceptible to forest fires in the Loja canton. In the training of the machine learning models, a multitemporal database with 1436 points was used, fed with the information from seven variables related to fuel moisture, proximity to anthropic activities, and ground elevation. After analyzing the performance of the three models, better results were observed with the LMT, thus offering application ease for local decision-makers. The results show that the technique used allowed generating a model with a good predictive capacity and that the maps resulting from the model can be updated in short periods of time. However, it is necessary to highlight the lack meteorological data availability at the local level and to encourage future researchers to implement improvements in this regard.

Hybrid models based on deep learning neural network and optimization algorithms for the spatial prediction of tropical forest fire susceptibility in Nghe An province, Vietnam

The main objective of this study was to produce forest fire susceptibility maps in the Nghe An province of Vietnam using machine learning models and GIS, namely Deep Neural Network (DNN), Hunger Games Search (HGS), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Adaboost (ADB), and Support Vector Machine (SVM). The application of these models included 1042 current and former forest fire points and 14 conditioning factors. The dataset was divided with a ratio of 70/30, with 70% for building the model and the remaining 30% for testing it. Each model was evaluated by various statistical indices and the results show that HGS performed best in constructing susceptibility maps and improving the performance of the DNN model compared to the reference models, with the areas under the receiver operating characteristic curves (AUROC) of 0.967 The findings of this research may support decision-makers on sustainable land-use planning.

Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do.

Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire

Increasing numbers and intensity of forest fires indicate that forests have become susceptible to fires in the tropics. We assessed the susceptibility of forests to fire in India by comparing six machine learning (ML) algorithms. We identified the best-suited ML algorithms for triggering a fire prediction model, using minimal parameters related to forests, climate and topography. Specifically, we used Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspots from 2001 to 2020 as training data. The Area Under the Receiver Operating Characteristics Curve (ROC/AUC) for the prediction rate showed that the Support Vector Machine (SVM) (ROC/AUC = 0.908) and Artificial Neural Network (ANN) (ROC/AUC = 0.903) show excellent performance. By and large, our results showed that north-east and central India and the lower Himalayan regions were highly susceptible to forest fires. Importantly, the significance of this study lies in the fact that it is possibly among the first to predict forest fire susceptibility in the Indian context, using an integrated approach comprising ML, Google Earth Engine (GEE) and Climate Engine (CE).

A deep learning ensemble model for wildfire susceptibility mapping

Devastating wildfires have increased in frequency and intensity over the last few years, worsened by climate change and prolonged droughts. Wildfire susceptibility mapping with machine learning has been proven useful for fire-prevention plans, turning into an indispensable tool in wildfire prevention. However, applications of deep learning models in wildfire susceptibility prediction to date are scarce. This study proposes a new Ensemble model based on two deep learning networks previously presented in literature that achieved remarkable results for forest fire susceptibility and other environmental risks. We compare our model with each of its sub-models, two more deep learning networks, and other machine learning benchmark, namely, XGBoost and SVM. Furthermore, we analyze the effects that different sample patch sizes have on the predictive performance of the algorithms. As case study we selected the fire occurrences in two regions in Chile, from 2013 to 2019. Satellite imagery data for fifteen fire influencing factors in the study area were retrieved to build a dataset to extract the samples to train the models. These factors include elevation, aspect, surface roughness, slope, minimum and maximum temperature, wind speed, precipitation, actual evapotranspiration, climatic water deficit, NDVI, land cover type, distance to rivers, distance to roads and distance to urban areas. During training, the best sample patch size was found to be 25×25 pixels. As a result, the highest area under the curve (AUC) was 0.953 achieved by the Ensemble model, followed by CNN-1 with AUC=0.902. The Ensemble model also achieved the best accuracy, sensitivity, specificity, negative predictive value and F1 score. Finally, the predicted susceptibility maps suggest that static variables can be considered as predisposing factors, while dynamic variables affect the intensity of the predicted probabilities, with an important role of the anthropogenic variables. These resulting maps may be useful to prioritize wildfire surveillance and monitoring in extensive high risk areas.

Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework

Forest fire is one of the foremost environmental disasters that threatens the Australian community. Recognition of the occurrence patterns of fires and the identification of fire risk is beneficial to mitigate probable fire threats. Machine learning techniques are recognized as well-known approaches to solving non-linearity problems such as forest fire risk. However, assessing such environmental multivariate disasters has always been challenging as modelling may be biased from multiple uncertainty sources such as the quality and quantity of input parameters, training processes, and a default setup for hyper-parameters. In this study, we propose a spatial framework to quantify the forest fire risk in the Northern Beaches area of Sydney. Thirty-six significant key indicators contributing to forest fire risk were selected and spatially mapped from different contexts such as topography, morphology, climate, human-induced, social, and physical perspectives as input to our model. Optimized deep neural networks were developed to maximize the capability of the multilayer perceptron for forest fire susceptibility assessment. The results show high precision of developed model against accuracy assessment metrics of ROC = 95.1%, PRC = 93.8%, and k coefficient = 94.3%. The proposed framework follows a stepwise procedure to run multiple scenarios to calculate the probability of forest risk with new input contributing parameters. This model improves adaptability and decision-making as it can be adapted to different regions of Australia with a minor localization adoption requirement of the weighting procedure.