A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau

文献类型： 外文期刊

第一作者： Wu, Ritu

作者： Wu, Ritu;Hong, Zhimin;Du, Wala;Shan, Yu;Ying, Hong;Wu, Rihan;Gantumur, Byambakhuu

作者机构：

关键词： Mongolian Plateau grassland fires; generalized boosted regression model; geographical weight; spatiotemporal weight; predictive models

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 9 期

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收录情况： SCI

摘要： Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models' (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests' (RF) 0.5662 and GBM's 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau.

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