基于改进Logistic回归模型在地质灾害评价中的应用
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摘要
为了解决传统Logistic回归模型中灾害点和随机点可能重叠的问题,该文提出了一种基于格网单元的二分Logistic回归模型。以四川省威远县为研究区,借助于遥感卫星影像、地质普查资料和地理国情监测数据,采用灾害点和格网单元2种方法的二分Logistic回归模型对威远县地质灾害进行评价,同时采用ROC曲线和已知点2种方验证评价结果。结果表明:在低山丘陵区域,基于格网单元的Logistic回归模型评价精度更高;威远县地质灾害中高易发区主要分布在西部、西北部山区,占全区总面积的39.35%;地层岩性、高程、坡度和距道路距离是导致地质灾害发生的主要因素。
In order to solve the problem that disaster points and random points may overlap in the traditional logistic regression model, a binary Logistic regression model based on grid elements was proposed. Weiyuan County of Sichuan Province was selected as research area. Data sets of remote-sensing satellite imagery and geological survey data and geographic conditions monitoring were exploited as the judgment parameters in evaluation model. The two-dimensional logistic regression model of disaster point and grid unit was used to evaluate the geological disasters in Weiyuan County. At the same time, the ROC curve and the known points were used to verify the evaluation results. The results show that the Logistic regression model based on the grid unit has higher evaluation accuracy in low hilly area. The high-prone areas in the County geological disasters, which account for 39.35% of the total area of the region, are mainly distributed in the western and northwestern mountainous areas. The main factors leading to geological disasters are elevation, slope and distance from the road.
In order to solve the problem that disaster points and random points may overlap in the traditional logistic regression model, a binary Logistic regression model based on grid elements was proposed. Weiyuan County of Sichuan Province was selected as research area. Data sets of remote-sensing satellite imagery and geological survey data and geographic conditions monitoring were exploited as the judgment parameters in evaluation model. The two-dimensional logistic regression model of disaster point and grid unit was used to evaluate the geological disasters in Weiyuan County. At the same time, the ROC curve and the known points were used to verify the evaluation results. The results show that the Logistic regression model based on the grid unit has higher evaluation accuracy in low hilly area. The high-prone areas in the County geological disasters, which account for 39.35% of the total area of the region, are mainly distributed in the western and northwestern mountainous areas. The main factors leading to geological disasters are elevation, slope and distance from the road.
引文
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[15]袁媛,陈新民,袁宗盼.基于Logistic模型的城市土地抗震适宜性评价方法研究[J].建筑结构,2013,43(S1):1479-1482.Yuan Yuan,Chen Xinmin,Yuan Zongpan.Research on evaluation method of urban land seismic suitability based on Logistic model[J].Building Structure,2013,43(S1):1479-1482.
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[17]王正鹏,谢志鹏,邱培超.语义关系相似度计算中的数据标准化方法比较[J].计算机工程,2012,38(10):38-40.Wang Zhengpeng,Xie Zhipeng,Qiu Peichao.Comparison of data standardization methods in semantic relation similarity calculation[J].Computer Engineering,2012,38(10):38-40.
[18]詹华勤.基于GIS川黔铁路贵州段地质灾害易发性评价研究[D].成都:西南交通大学,2017:60-66.Zhan Huaqin.Research on Evaluating the Easiness of Geological Disasters in Guizhou Section of Sichuan-Yunnan Railway Based on GIS[D].Chengdu:Southwest Jiaotong University,2017:60-66.
[2]文海涛,韦朝华,廖丽萍,等.桂东南容县地质灾害发育与时空分布特征[J].水土保持通报,2017,37(5):182-188.Wen Haitao,Wei Chaohua,Liao Liping,et al.Occurrence and temporal-spatial distribution characteristics of geological hazards in Rongxian County in Southeast Guangxi Zhuang Autonomous Region[J].Bulletin of Soil and Water Conservation,2017,37(5):182-188.
[3]Ayalew L,Yamagishi H,Ugawa N.Landslide susceptibility mapping using GIS-based weighted linear combination,the case in Tsugawa area of Agano River,Niigata Prefecture,Japan[J].Landslides,2004,1(1):73-81.
[4]宫清华,黄光庆,张冬良,等.基于斜坡单元的浅层滑坡风险区划:以华南松岗河小流域为例[J].安全与环境学报,2017,17(2):615-620.Gong Qinghua,Huang Guangqing,Zhang Dongliang,et al.Risk zoning of shallow landslide based on slope unit:taking Songganghe small watershed in South China as an example[J].Journal of Safety and Environment,2017,17(2):615-620.
[5]郭长宝,杜宇本,张永双,等.川西鲜水河断裂带地质灾害发育特征与典型滑坡形成机理[J].地质通报,2015,34(1):121-134.Guo Changbao,Du Yuben,Zhang Yongshuang,et al.Geological hazard development characteristics of Xianshuihe fault zone in western Sichuan and formation mechanism of typical landslide[J].Geological Bulletin of China,2015,34(1):121-134.
[6]邓辉,何政伟,陈晔,等.信息量模型在山地环境地质灾害危险性评价中的应用:以四川泸定县为例[J].自然灾害学报,2014,23(2):67-76.Deng Hui,He Zhengwei,Chen Ye,et al.Application of information model in risk assessment of mountain environmental geological hazards:a case study of Luding County,Sichuan Province[J].Journal of Natural Disasters,2014,23(2):67-76.
[7]关颖,程瑶,王东兴,等.新疆大区域滑坡地质灾害危险性评价研究[J].测绘工程,2018,27(6):26-31.Guan Ying,Cheng Yao,Wang Dongxing,et al.Study on risk assessment of geological hazards in large areas of Xinjiang[J].Engineering of Surveying and Mapping,2018,27(6):26-31,40.
[8]王卫东,钟晟.基于GIS的Logistic回归模型在地质灾害危险性区划中的应用[J].工程勘察,2009,37(11):5-10.Wang Weidong,Zhong Sheng.Application of Logistic regression model based on GIS in geological hazard zoning[J].Geotechnical Investigation&Surveying,2009,37(11):5-10.
[9]樊芷吟,苟晓峰,秦明月,等.基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价[J].工程地质学报,26(2):340-347.Fan Zhiyin,Gou Xiaofeng,Qin Mingyue,et al.Information and Logistic regression models based coupling analysis for susceptibility of geological hazards[J].Journal of Engineering Geology,26(2):340-347.
[10]杜谦,范文,杨德宏,等.二元Logistic回归和信息量模型在地质灾害分区中的应用[J].灾害学,2017,32(2):220-226.Du Qian,Fan Wen,Yang Dehong,et al.Application of binary Logistic regression and information model in geological hazard zoning[J].Journal of Catastrophology,2017,32(2):220-226.
[11]徐志文.四川省地质环境状况及地质灾害发育特征研究[J].地质与勘探,2006(4):97-102.Xu Zhiwen.Study on the geological environment and the development characteristics of geological disasters in Sichuan Province[J].Geology and Prospecting,2006(4):97-102.
[12]任茜,樊宏,贾国君,等.低山丘陵区土地利用格局优化研究:以四川省内江市为例[J].安徽农业科学,2010,38(4):2149-2152.Ren Qian,Fan Hong,Jia Guojun,et al.Study on optimization of land use pattern in low mountain and hilly area:taking Neijiang City of Sichuan Province as an example[J].Journal of Anhui Agriculture Science,2010,38(4):2149-2152.
[13]方苗,张金龙,徐瑱.基于GIS和Logistic回归模型的兰州市滑坡灾害敏感性区划研究[J].遥感技术与应用,2011,26(6):845-854.Fang Miao,Zhang Jinlong,Xu Zhen.Landslide susceptibility zoning study in Lanzhou City based on GIS and Logistic regression model[J].Remote Sensing Technology and Application,2011,26(6):845-854.
[14]牛彦波,宫阿都,尹晓天,等.基于Logistic回归分析的穆棱市地质灾害风险分析[J].国土与自然资源研究,2014(3):76-79.Niu Yanbo,Gong Adu,Ying Xiaotian,et al.Risk analysis of geological disasters in Muling City based on Logistic regression analysis[J].Territory&Natural Resources Study,2014(3):76-79.
[15]袁媛,陈新民,袁宗盼.基于Logistic模型的城市土地抗震适宜性评价方法研究[J].建筑结构,2013,43(S1):1479-1482.Yuan Yuan,Chen Xinmin,Yuan Zongpan.Research on evaluation method of urban land seismic suitability based on Logistic model[J].Building Structure,2013,43(S1):1479-1482.
[16]倪忠云.西藏曲水-桑日地区生态地质环境承载力研究[D].成都:成都理工大学,2011:124-126.Ni Zhongyun.Study on the Carrying Capacity of Ecological Geological Environment in Qushui-Sangri Area of Tibet[D].Chengdu:Chengdu University of Technology,2011:124-126.
[17]王正鹏,谢志鹏,邱培超.语义关系相似度计算中的数据标准化方法比较[J].计算机工程,2012,38(10):38-40.Wang Zhengpeng,Xie Zhipeng,Qiu Peichao.Comparison of data standardization methods in semantic relation similarity calculation[J].Computer Engineering,2012,38(10):38-40.
[18]詹华勤.基于GIS川黔铁路贵州段地质灾害易发性评价研究[D].成都:西南交通大学,2017:60-66.Zhan Huaqin.Research on Evaluating the Easiness of Geological Disasters in Guizhou Section of Sichuan-Yunnan Railway Based on GIS[D].Chengdu:Southwest Jiaotong University,2017:60-66.