Preprints
https://doi.org/10.5194/nhess-2022-72
https://doi.org/10.5194/nhess-2022-72
12 Apr 2022
 | 12 Apr 2022
Status: this preprint has been withdrawn by the authors.

Model Comparisons for Predicting Grassland Fire Occurrence Probability in Inner Mongolia Autonomous Region, China

Chang Chang, Yu Chang, Zaiping Xiong, Xiaoying Ping, Heng Zhang, Meng Guo, and Yuanman Hu

Abstract. Grassland fires threaten grassland ecosystem, human life and economic development greatly. However, there are limited researches focusing on grassland fire prediction, thereby it is necessary to find a better method to predict probability of grassland fire occurrence. Here we selected 16 environmental variables that may have impacts on fire occurrence, then built regression models of grassland fire probability based on historical fire points and variables in Inner Mongolia by three methods to identify the grassland fire drivers and to predict fire probability. The three methods were global logistic regression, geographically weighted logistic regression and random forest. The results showed that random forest model had the best predictive effect. The influence of 9 variables selected by geographically weighted logistic regression model on grassland fire was unbalanced spatially. The three models all showed that meteorological factors were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurred in different areas had various responses to the influencing drivers, and the areas with different natural and geographical condition had different fire prevention periods. Thus, the grassland fire management strategy based on local conditions should be advocated.

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Chang Chang, Yu Chang, Zaiping Xiong, Xiaoying Ping, Heng Zhang, Meng Guo, and Yuanman Hu

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-72', Anonymous Referee #1, 27 Jul 2022
    • AC1: 'Reply on RC1', Chang Chang, 27 Jul 2022
  • RC2: 'Comment on nhess-2022-72', Anonymous Referee #2, 29 Jul 2022
    • AC2: 'Reply on RC2', Chang Chang, 01 Aug 2022
  • RC3: 'Comment on nhess-2022-72', Anonymous Referee #3, 22 Aug 2022
    • AC3: 'Reply on RC3', Chang Chang, 08 Sep 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-72', Anonymous Referee #1, 27 Jul 2022
    • AC1: 'Reply on RC1', Chang Chang, 27 Jul 2022
  • RC2: 'Comment on nhess-2022-72', Anonymous Referee #2, 29 Jul 2022
    • AC2: 'Reply on RC2', Chang Chang, 01 Aug 2022
  • RC3: 'Comment on nhess-2022-72', Anonymous Referee #3, 22 Aug 2022
    • AC3: 'Reply on RC3', Chang Chang, 08 Sep 2022
Chang Chang, Yu Chang, Zaiping Xiong, Xiaoying Ping, Heng Zhang, Meng Guo, and Yuanman Hu

Data sets

China monthly Vegetation index (NDVI) spatial distribution dataset Xinliang Xu https://doi.org/10.12078/2018060602

Model code and software

Windows Application for Geographically Weighted Regression Modelling Tomoki Nakaya https://download.csdn.net/download/xiaodongfly/7027693

Chang Chang, Yu Chang, Zaiping Xiong, Xiaoying Ping, Heng Zhang, Meng Guo, and Yuanman Hu

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Short summary
Random forest model had the highest fitting goodness to Inner Mongolia grassland fires from 2000 to 2018. The influence of 9 drivers on grassland fire was spatially unbalanced. Meteorological factors were of great importance to grassland fire. In Inner Mongolia, different areas had different sensitivities to different drivers. Thus, the grassland fire management strategy based on local conditions should be advocated.
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