Comparing calibrated statistical and machine learning methods for wildland fire occurrence prediction: a case study of human-caused fires in Lac La Biche, Alberta, Canada

Phelps, Nathan; Woolford, Douglas G.

doi:10.1071/wf20139

Cited by 18 publications

(6 citation statements)

References 63 publications

(79 reference statements)

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“…Machine learning techniques have been widely used to predict wildfire probability, due to their convenience in integrating multisource data and higher accuracy than traditional statistical methods and fire weather or drought indices approach (Goldarag et al 2016;Cao et al 2017;Jaafari, Mafi-Gholami et al 2019;Zhang et al 2019;Phelps and Woolford 2021). One of the critical problems in wildfire probability modeling is the elimination of multicollinearity in the input variables.…”

Section: Discussionmentioning

confidence: 99%

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

Chen

Quan

et al. 2023

Int J Disaster Risk Sci

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Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly the temporal characteristics of weather variables in a given period, are paramount in predicting the probability of wildfire occurrence. However, rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period, introducing additional uncertainties in wildfire probability modeling. To solve the problem, this study employed the weather variables in continuous nonprecipitation days as the “dynamic-step” weather variables with which to improve wildfire probability modeling. Multisource data on weather, fuel, topography, infrastructure, and derived variables were used to model wildfire probability based on two machine learning methods—random forest (RF) and extreme gradient boosting (XGBoost). The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models. The variable importance analysis also verified the top contribution of these dynamic-step weather variables, indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.

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Section: Discussionmentioning

confidence: 99%

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

Chen

Quan

et al. 2023

Int J Disaster Risk Sci

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“…Logistic regression (LR) is a modelling technique that is part of the family of generalized linear models [132] and is one of the popular statistical models utilized effectively to predict fire occurrence and examine the driving factors for fire ignition and propagation. In a regression model, an equation for predicting the value of the dependant variable based on one or more independent predictor variables is developed [133].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques

Mofokeng,

Adelabu,

Jackson

2024

Fire

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Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.

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“…A few of them are related to bushfire severity quantification. Most of the ML-based severity research utilises RF model in bushfire severity pattern mapping [23], [42], [45], influencing driver analysis and severity predictive assessment [3], [4], [8], [33], [46]- [49]. In addition, different other models including GB and SVM and their ensembles have demonstrated their capability in explaining complex variable relationships in bushfires applications [50]- [53].…”

Section: B Bushfire Severity Modellingmentioning

confidence: 99%

Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

Sharma

Aryal

Shao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Topography plays a significant role in determining bushfire severity over a hilly landscape. However, complex interrelationships between topographic variables and bushfire severity are difficult to quantify using traditional statistical methods. More recently, different Machine Learning (ML) models are becoming popular in characterising complex relationships between different environmental variables. Yet, few studies have specifically evaluated the suitability of ML models in predictive bushfire severity analysis. Hence, the aim of this research is twofold. First, to determine suitable ML models by assessing their performances in bushfire severity predictions using remote sensing data analytics, and second to identify and investigate topographic variables influencing bushfire severity. The results showed that Random Forest (RF) and Gradient Boosting (GB) models had their distinct advantages in predictive modelling of bushfire severity. RF model showed higher precision (86% to 100%) than GB (59% to 72%) while predicting low, moderate, and high severity classes. Whereas GB model demonstrated better recall, i.e., completeness of positive predictions (56% to 75%) than RF (49% to 61%) for those classes. Closer investigations on topographic characteristics showed a varying relationship of severity patterns across different morphological landform classes. Landforms having lower slope curvatures or with unchanging slopes were more prone to severe burning than those landforms with higher slope curvatures. Our results provide insights into how topography influences potential bushfire severity risks and recommends purpose-specific choice of ML models.

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Comparing calibrated statistical and machine learning methods for wildland fire occurrence prediction: a case study of human-caused fires in Lac La Biche, Alberta, Canada

Cited by 18 publications

References 63 publications

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques

Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

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