Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Abujayyab, Sohaib K. M.; Kassem, Moustafa Moufid; Khan, Ashfak Ahmad; Wazirali, Raniyah; Coşkun, Mücahit; Taşoğlu, Enes; Öztürk, Ahmet; Toprak, Ferhat

doi:10.1155/2022/3959150

Cited by 15 publications

(5 citation statements)

References 77 publications

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“…This conclusion aligns with the findings of other researchers [48,[103][104][105][106]. Moreover, previous studies investigating wildfire susceptibility prediction, including the utilization of XGBoost, have also reported its strong performance [107][108][109][110].…”

Section: Comparison Of ML Algorithms and Importance Of Conditioning F...supporting

confidence: 91%

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

et al. 2023

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The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as the basis, and utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM), and eXtreme gradient boosting (XGBoost), to assess wildfire susceptibility. By evaluating multiple indicators, we obtained the optimal model and used the Shapley Additive Explanations (SHAP) method to explain the effects of the factors and the decision-making mechanism of the model. In addition, we collected and calculated corresponding indicators, with the Remote Sensing Ecological Index (RSEI) representing ecological vulnerability and the Night-Time Lights Index (NTLI) representing urban development vulnerability. The coupling results of the two represent the comprehensive vulnerability of the ecology and city. Finally, by integrating wildfire susceptibility and vulnerability information, we assessed the risk of wildfire disasters in Guilin to reveal the overall distribution characteristics of wildfire disaster risk in Guilin. The results show that the AUC values of the eight models range from 0.809 to 0.927, with accuracy values ranging from 0.735 to 0.863 and RMSE values ranging from 0.327 to 0.423. Taking into account all the performance indicators, the XGBoost model provides the best results, with AUC, accuracy, and RMSE values of 0.927, 0.863, and 0.327, respectively. This indicates that the XGBoost model has the best predictive performance. The high-susceptibility areas are located in the central, northeast, south, and southwest regions of the study area. The factors of temperature, soil type, land use, distance to roads, and slope have the most significant impact on wildfire susceptibility. Based on the results of the ecological vulnerability and urban development vulnerability assessments, potential wildfire risk areas can be identified and assessed comprehensively and reasonably. The research results of this article not only can improve the specificity and practical significance of wildfire prediction models but also provide important reference for the prevention and response of wildfires.

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Section: Comparison Of ML Algorithms and Importance Of Conditioning F...supporting

confidence: 91%

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

et al. 2023

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“…Accuracy is a metric used to evaluate the overall predictive performance of the model, and it reflects the model's accuracy in predicting all of the samples. Kappa'C considers the consistency and randomness of the model's prediction and the actual observation, which can more accurately evaluate the model's classification performance [69]. A higher Kappa'C value indicates more reliable prediction results from the model.…”

Section: Performance Assessmentmentioning

confidence: 99%

Method of Wildfire Risk Assessment in Consideration of Land-Use Types: A Case Study in Central China

Yue

Ren

Liang

et al. 2023

Forests

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Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control.

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“…Forests are one of the most important natural resources on Earth, and forest ecosystems play a crucial role in maintaining biodiversity, species composition and ecosystem structure. In recent years, there has been a significant increase in the frequency of forest fire incidents and the extent of the areas affected, indicating the magnitude of the problem [1]. Forest fires are becoming more common, in part because of global warming, which disrupts the stability of the rainy season as summers become hotter and drier than before and winds become stronger [2].…”

Section: Introductionmentioning

confidence: 99%

Four hybrid machine learning algorithms to predict forest fire susceptibility

Zhang,

Huang,

Ouyang

et al. 2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Cited by 15 publications

References 77 publications

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

Method of Wildfire Risk Assessment in Consideration of Land-Use Types: A Case Study in Central China

Four hybrid machine learning algorithms to predict forest fire susceptibility

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