Volume 14 Issue 2
Apr.  2023
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Rui Chen, Binbin He, Xingwen Quan, Xiaoying Lai, Chunquan Fan. Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China[J]. International Journal of Disaster Risk Science, 2023, 14(2): 313-325. doi: 10.1007/s13753-023-00476-z
Citation: Rui Chen, Binbin He, Xingwen Quan, Xiaoying Lai, Chunquan Fan. Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China[J]. International Journal of Disaster Risk Science, 2023, 14(2): 313-325. doi: 10.1007/s13753-023-00476-z

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

doi: 10.1007/s13753-023-00476-z

(4) the National Catalogue Service for Geographic Information (NCSFGI, http://www.webmap.cn/main.do?method=index) providing the original dataset for this study

(3) the European Center for Medium-Range Weather Forecasts (ECMWF, http://apps.ecmwf.int) providing ERA5-land dataset

This work was supported by the National Natural Science Foundation of China (Contract no. U20A2090). The authors are grateful to the dataset providers:(1) the Land Processes Distributed Active Archive Center (LPDAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) (http://lpdaac.usgs.gov) providing MODIS product

and (5) the Sichuan Forestry and Grassland Administration providing the forest resource survey data of Sichuan Province (2010-2020).

(2) the United States Geological Survey, USGS National Geospatial-Intelligence Agency, NGA providing the Global Multi-resolution Terrain Elevation Data 2010 and GMTED2010 digital elevation model (DEM)

  • Accepted Date: 2023-02-03
  • Available Online: 2023-04-28
  • Publish Date: 2023-03-20
  • Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly the temporal characteristics of weather variables in a given period, are paramount in predicting the probability of wildfire occurrence. However, rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period, introducing additional uncertainties in wildfire probability modeling. To solve the problem, this study employed the weather variables in continuous nonprecipitation days as the "dynamic-step" weather variables with which to improve wildfire probability modeling. Multisource data on weather, fuel, topography, infrastructure, and derived variables were used to model wildfire probability based on two machine learning methods-random forest (RF) and extreme gradient boosting (XGBoost). The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models. The variable importance analysis also verified the top contribution of these dynamic-step weather variables, indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.
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