A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau
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...
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2025-04-01
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| author | Ritu Wu Zhimin Hong Wala Du Yu Shan Hong Ying Rihan Wu Byambakhuu Gantumur |
| author_facet | Ritu Wu Zhimin Hong Wala Du Yu Shan Hong Ying Rihan Wu Byambakhuu Gantumur |
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| description | 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. |
| format | Article |
| id | doaj-art-0484d4c3cde6455bbff71a0437fc4408 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-0484d4c3cde6455bbff71a0437fc44082025-08-20T02:59:08ZengMDPI AGRemote Sensing2072-42922025-04-01179148510.3390/rs17091485A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian PlateauRitu Wu0Zhimin Hong1Wala Du2Yu Shan3Hong Ying4Rihan Wu5Byambakhuu Gantumur6Science of Collage, Inner Mongolia University of Technology, Hohhot 010051, ChinaScience of Collage, Inner Mongolia University of Technology, Hohhot 010051, ChinaInstitute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010022, ChinaCollege of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, ChinaDepartment of Geography, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14200, MongoliaGrassland 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.https://www.mdpi.com/2072-4292/17/9/1485Mongolian Plateau grassland firesgeneralized boosted regression modelgeographical weightspatiotemporal weightpredictive models |
| spellingShingle | Ritu Wu Zhimin Hong Wala Du Yu Shan Hong Ying Rihan Wu Byambakhuu Gantumur A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau Remote Sensing Mongolian Plateau grassland fires generalized boosted regression model geographical weight spatiotemporal weight predictive models |
| title | A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau |
| title_full | A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau |
| title_fullStr | A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau |
| title_full_unstemmed | A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau |
| title_short | A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau |
| title_sort | generalized spatiotemporally weighted boosted regression to predict the occurrence of grassland fires in the mongolian plateau |
| topic | Mongolian Plateau grassland fires generalized boosted regression model geographical weight spatiotemporal weight predictive models |
| url | https://www.mdpi.com/2072-4292/17/9/1485 |
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