Probability models of forest fire risk based on ecology factors in different vegetation regions over China

Xiaowei, 李晓炜 Li; Gang, 赵刚 Zhao; Xiubo, 于秀波 Yu; Qiang, 于强 Yu

doi:10.5846/stxb201205270783

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Among the vegetation factors, the effect of the current month's NDVI and the previous month's NDVI is greater. NDVI is a good indicator of the growth of vegetation, and as vegetation cover increases, the combustible fuel is higher and the probability of fires is greater [44,86].…”

Section: The Main Factors Responsible For Wildfirementioning

confidence: 99%

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Peng,

Wei,

Chen

et al. 2023

Forests

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Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.

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Section: The Main Factors Responsible For Wildfirementioning

confidence: 99%

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Peng,

Wei,

Chen

et al. 2023

Forests

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“…We classify the data collected by temperature sensors, smoke sensors, humidity sensors and gas sensors (Hongyuan Ge, 2018), as well as the number of sunny days and the number of flammable plants, as a secondary feature node of the Bayesian network (Xiaowei Li, 2013). Value of the feature node is divided into four levels: general, high, high, and very high, and the values are 0/1/2/3/4 respectively.…”

Section: Data Classification Preprocessingmentioning

confidence: 99%

Forest Fire Prevention Early Warning Method Based on Fuzzy Bayesian Network

Lin

Dong

Liu

et al. 2019

SET

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In the environment of large forest, the factors causing fire are nonlinear and uncertain. If the data collected by the sensor is simply analyzed and compared, the false alarm rate will be higher. How to combine the data of several sensors for effective fire warning is a difficult point. In order to improve the accuracy of prediction, aiming at the shortcomings of traditional forest fire prevention early warning system, we propose a forest fire prevention early warning method based on fuzzy Bayesian network. Firstly, we combine the fuzzy control system and the Bayesian network in series, and pre-process the collected sensor data. The pre-processed data is sent to the previously trained Bayesian network for processing. Then the calculated open fire probability, smoldering fire probability, and no fire probability are used as input data of fuzzy control system, and fuzzy inference is performed. Finally, we de-fuzzify the results of fuzzy reasoning and get the probability of fire. Simulation results show that our method can effectively combine the data collected by multiple sensors, quickly and accurately determine fire occurrence probability, improve the accuracy of forest fire prevention warning, and reduce the false positive rate.

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Probability models of forest fire risk based on ecology factors in different vegetation regions over China

Cited by 2 publications

References 6 publications

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Forest Fire Prevention Early Warning Method Based on Fuzzy Bayesian Network

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