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

Sharma, Saroj Kumar; Aryal, Jagannath; Shao, Quanxi; Rajabifard, Abbas

doi:10.1109/jstars.2023.3249643

Cited by 3 publications

(3 citation statements)

References 96 publications

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“…The efficiency of RF is influenced by two parameters: the number of trees in the forest (ntree) and the number of random variables per split node (mtry) [138,139]. Sharma, et al [26] thoroughly explained the RF algorithm for the RF classification and regression algorithms.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…This pattern might be due to the effect of climate warming. Although the TRI had collinearity with other topographic drivers [26], its superiority to elevation underscores its incorporation in fire-danger modelling.…”

Section: The Driving Factors Of Fire-danger-assessment Modellingmentioning

confidence: 99%

“…In addition, remote sensing (RS) is a popular method in fire modelling. RS is used to classify land cover which is associated with fire-fuel characteristics [25], provide elevation data [26], assess vegetation moisture content [7,[27][28][29], assess soil moisture content [10,[30][31][32], and for active fire detection and burnt-area estimation [33][34][35]. With the development of Google Earth Engine (GEE) and Cloud Engine (CE) [36], platforms that reduce the requirement for expensive information technology infrastructure, the acquiring and processing of RS datasets is rapidly improving the prediction of fire-danger models [37].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Random Forest (Rf)mentioning

confidence: 99%

Section: The Driving Factors Of Fire-danger-assessment Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Paying Attention to Wildfire: Using U-Net with Attention Blocks on Multimodal Data for Next Day Prediction

Fitzgerald,

Seefried,

Yost

et al. 2023

International Conference on Multimodal Interaction

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SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye

Iban,

Aksu

2024

Remote Sensing

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Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability.

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

Cited by 3 publications

References 96 publications

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

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

Paying Attention to Wildfire: Using U-Net with Attention Blocks on Multimodal Data for Next Day Prediction

SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye

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