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

Sun, Yanyan; Zhang, Fuquan; Lin, Haifeng; Xu, Shuwen

doi:10.3390/rs14174362

Cited by 22 publications

(9 citation statements)

References 81 publications

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“…We trained on four types of machine learning models using the training set and evaluated the performance of each model with the help of the validation set. Finally, we visualized the model results and drew a fire risk map of the research area [43,44]. The detailed process is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

Shi

Zhang

2023

Forests

Self Cite

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The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased model performance. To address this, our study applies the particle swarm optimization (PSO) algorithm to select the optimal parameters of the RF model, with the goal of enhancing model performance. We employ the optimized ensemble model (PSO-RF) to create a fire risk map for Jiushan National Forest Park in Anhui Province, China, thereby filling the research gap in this region’s forest fire studies. Based on collinearity tests and previous research results, we selected eight fire driving factors, including topography, climate, human activities, and vegetation for modeling. Additionally, we compare the logistic regression (LR), support vector machine (SVM), and RF models. Lastly, we select the optimal model to evaluate feature importance and generate the fire risk map. Model evaluation results demonstrate that the PSO-RF model performs best (AUC = 0.908), followed by RF (0.877), SVM (0.876), and LR (0.846). In the fire risk map created by the PSO-RF model, 70.73% of the area belongs to the normal management zone, while 15.23% is classified as a fire alert zone. The feature importance analysis of the PSO-RF model reveals that the NDVI is the key fire driving factor in this study area. Through utilizing the PSO algorithm to optimize the RF model, we have addressed the subjective and empirical problems of the RF model hyperparameter setting, thereby enhancing the model’s accuracy and generalization ability.

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Section: Methodsmentioning

confidence: 99%

A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

Shi

Zhang

2023

Forests

Self Cite

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“…Although XGBoost is regarded as a state-of-the-art evaluator with ultra-high performance in both classification and regression tasks, its efficiency and scalability are not satisfactory in the presence of high feature dimensions and large data sizes (Wu, 2020;Wang et al, 2021). In contrast, LightGBM, released by Microsoft in late 2017 (Ke et al, 2017), emerges as a novel gradient boosting technique designed to address the limitations of traditional boosting algorithms, including high memory usage, computational complexity and time consumption (Sun et al, 2022).…”

Section: Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Unfrozen Water Content Estimation: A Comparison between Ensemble and Non-ensemble Machine Learning Models

Li,

Zhou,

Ren

et al. 2024

Preprint

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Unfrozen water content (UWC) is a key parameter affecting a variety of soil physical-mechanical properties and processes in frozen soil systems. However, traditional estimation models suffer limitations due to oversimplified assumptions or limited applicable conditions. Given that, there is a compelling need to explore alternative modeling approaches that leverage machine learning (ML) algorithms, which have shown increasing potential in engineering fields. To this end, this study evaluated and compared six widely known ML algorithms (i.e., three ensemble models: RF, LightGBM and XGBoost; and three non-ensemble models: KNN, SVR and BPNN) for modeling UWC based on collected experimental datasets. These algorithms were optimized and evaluated using a framework combining Bayesian optimization and cross-validation to ensure model stability and generalization. The results demonstrated that the ensemble tree-based methods, particularly LightGBM and XGBoost, achieved the highest predictive accuracy and superior overall performance. On the other hand, the nonensemble methods exhibited poorer generalization abilities. Interestingly, during 10-fold cross-validation, consistent underperformance was observed for a particular fold, possibly stemming from the challenges of the data distribution in that fold after random shuffling. The present study highlights the effectiveness of ensemble learning approaches, importance of proper hyperparameter tuning and validation strategies, and intrinsic modeling challenges arising from the difference between the freezing and thawing phase change behaviors. This comprehensive ML model comparison and robust training framework provide valuable guidance on selecting suitable data-driven techniques for modeling frozen soil properties for cold regions hydrogeology and engineering practices.

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“…Sun et al [ 88 ] proposed a development of a forest fire susceptibility model using the LightGBM (an ensemble learning method - short for light gradient-boosting machine) for Nanjing Laoshan National Forest Park, resulting in an accurate fire susceptibility map. Eight variables related to topography, climate, human activity, and vegetation were selected for modeling based on correlation analysis.…”

Section: The Role Of Traditional Machine Learning and Deep Learning I...mentioning

confidence: 99%

Advancements in Forest Fire Prevention: A Comprehensive Survey

Carta,

Zidda,

Putzu

et al. 2023

Sensors

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Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires.

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

Cited by 22 publications

References 81 publications

A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

Unfrozen Water Content Estimation: A Comparison between Ensemble and Non-ensemble Machine Learning Models

Advancements in Forest Fire Prevention: A Comprehensive Survey

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