基于GWR模型的江西省山洪灾害区域异同性研究

苗月鲜; 方秀琴; 吴小君; 吴陶樱

doi:10.13961/j.cnki.stbctb.2018.01.054

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基于GWR模型的江西省山洪灾害区域异同性研究

试验研究

- 基于GWR模型的江西省山洪灾害区域异同性研究
- Regional Similarities and Differences of Mountain Torrent Disaster in Jiangxi Province Based on Geographically Weighted Regression
- 水土保持通报 2018年38卷第1期页码：313-318
- 作者机构：
  
  河海大学地球科学与工程学院,江苏,南京,211100
- 作者简介：
- 基金信息：
  
  国家重点研发计划项目“陆地水循环演变及其在全球变化中的作用研究”（2016YFA0601500）;中央高校基本科研业务费专项资金（2016B05414）
- DOI：10.13961/j.cnki.stbctb.2018.01.054
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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苗月鲜, 方秀琴, 吴小君, 等. 基于GWR模型的江西省山洪灾害区域异同性研究[J]. 水土保持通报, 2018,38(1):313-318.

MIAO Yuexian, FANG Xiuqin, WU Xiaojun, et al. Regional Similarities and Differences of Mountain Torrent Disaster in Jiangxi Province Based on Geographically Weighted Regression[J]. Bulletin of Soiland Water Conservation, 2018, 38(1): 313-318.
苗月鲜, 方秀琴, 吴小君, 等. 基于GWR模型的江西省山洪灾害区域异同性研究[J]. 水土保持通报, 2018,38(1):313-318. DOI： 10.13961/j.cnki.stbctb.2018.01.054.

MIAO Yuexian, FANG Xiuqin, WU Xiaojun, et al. Regional Similarities and Differences of Mountain Torrent Disaster in Jiangxi Province Based on Geographically Weighted Regression[J]. Bulletin of Soiland Water Conservation, 2018, 38(1): 313-318. DOI： 10.13961/j.cnki.stbctb.2018.01.054.

摘要

[目的]探索山洪灾害空间分布的规律，为江西省山洪灾害防治和各流域的监测和管理提供重要决策支持。[方法]根据山洪灾害的形成机理，从触发因子、孕灾环境、承灾体3个方面选取9个解释变量，将山洪灾害调查数据的5项内容作为反应变量，并作为评价山洪灾害度的指标，利用地理加权回归方法（GWR）构建模型，然后利用GIS技术探讨江西省3个不同区域山洪灾害空间分布的异同性。[结果]同一区域不同山洪灾害度指标的模型之间具有异同性，不同区域同一山洪灾害度指标的模型之间也具有异同性，不同灾害度指标的空间分布也表现出明显的异同性。[结论]在构建各项灾害度指标模型时，不仅要考虑到地域上的差异，也要考虑到不同灾害度指标之间的差异，GWR模型能有效地解释局部空间变化情况和重要解释变量的分异性。

Abstract

[Objective] The study aims to investigate the spatial distribution of mountain torrent disasters in order to provide support for the prevention and management of mountain torrent disaster in overall Jiangxi Province and river basins.[Methods] Based on formation mechanism of mountain torrent disasters

nine explanatory variables were selected from triggering factors

disaster inducing environment and disaster-bearing body. Five response variables from the survey data of mountain torrent disaster were used as the indices to evaluate the hazard degree. The spatial patterns of mountain torrent disaster were estimated based on the geographically weighted regression (GWR) method. The similarities and differences of mountain torrent disaster in three different regions of Jiangxi Province were analyzed using GIS technology.[Results] There were similarities and differences among the models of different mountain torrent disaster indicators in the same area

the same mountain torrent disaster indicators in different regions and the spatial distribution of mountain torrent disaster indicators.[Conclusion] Not only the geographical differences but also the differences between different disaster degree indicators should be taken into account in building each model. GWR can effectively explain the local spatial variability and the differences of important explanatory variables.

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references

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