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 sus...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 14; no. 17; p. 4362
Main Authors: Sun, Yanyan, Zhang, Fuquan, Lin, Haifeng, Xu, Shuwen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2022
Subjects:
Algorithms
Altitude
case studies
China
Comparative analysis
Correlation analysis
Datasets
fire factors
Fire prevention
fire susceptibility map
Forest & brush fires
Forest fires
Heat
humans
Humidity
Machine learning
Model accuracy
Modelling
National forests
Performance evaluation
prediction
regression analysis
Resource allocation
Susceptibility
temperature
Topography
Vegetation
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs14174362