基于信息量模型的涪陵区地质灾害易发性评价

doi:10.3724/SP.J.1047.2015.01554

摘要/Abstract

摘要：

本文以重庆涪陵区为研究区域,选取坡度、坡向、累计汇水面积、地层岩性、水域、降雨量、植被和土地利用分类8个影响因子,提取高分一号遥感数据（2013.12.24）动态影响因子,引入信息量模型,分别计算上述影响因子对应的信息量,对该时期示范区的地质灾害危险性进行评价,并引入ROC曲线和AUC评价指标,对得到的区域地质灾害易发性评价结果进行精度评估。结果显示,2013年12月研究区内高易发区面积占总面积的9.73%,该易发区内含有104个地质灾害点,占所有灾害点的52.7%,灾积比为5.42,明显大于其他易发等级类别。利用ROC评价方法,计算成功率曲线AUC为0.796,预测率曲线AUC为0.748（74.8%）,具有较高的可靠性,证明本文方法在该区域地质灾害易发性评价的适应性良好。

关键词: 地质灾害, 易发性评价, 信息量模型, 动态影响因子

Abstract:

Using geospatial technologies to assess geological hazard risk has been proved feasible, effective and important in the southwest of China, which is featured by mountainous landscape and the population density is very large. The main objective of this study is to make the risk assessment of the geological hazards in Fuling district using information quantity model, and eight triggering factors are used, including slope, aspect, cumulative catchment area, formation lithology, distances to water, precipitation, vegetation, and land use/land cover type respectively. GaoFen-1 image of December 24, 2013 is used to extract two dynamic triggering factors, vegetation and land use, and precipitation is also taken as a dynamic triggering factor. All triggering factors were then used to construct an information model to assess and predict the geological hazards in the study area in December 2013, producing a geological hazard risk distribution map. Finally, ROC curve was used to validate the information model. The statistical results indicate that the areas with high risk zone is about 9.73% of the entire area and that the percentage of the geological hazards sites is about 52.7% of the entire geological hazards sites. And it shows a satisfactory consistency between the susceptibility map and the geological hazard locations. The AUC of success-rate ROC of 0.796 and the AUC of prediction-rate ROC of 0.748 demonstrate the robustness and relatively good reliability of the information quantity model. Above all, the model can be applied to interpret and predict the geological hazard occurrences in the study area.

Key words: Geological hazard, risk assessment, information quantity, dynamic triggering factors

谭玉敏, 郭栋, 白冰心, 许波. 基于信息量模型的涪陵区地质灾害易发性评价[J]. 地球信息科学学报, 2015, 17(12): 1554-1562.DOI:10.3724/SP.J.1047.2015.01554

TAN Yumin,GUO Dong,BAI Bingxin,XU Bo. Geological Hazard Risk Assessment Based on Information Quantity Model in Fuling District, Chongqing City, China[J]. Journal of Geo-information Science, 2015, 17(12): 1554-1562.DOI:10.3724/SP.J.1047.2015.01554

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站点	日降雨	rain4d	rain3d	rain2d	rain1d
白涛	0	0.5	0	0	0
百胜	0	0.8	0	0.1	0.1
从林	0	1.7	0	0.1	0.5
大木	0.1	2.5	0.1	0	0.1
大溪	0	1.7	0	0	0.2
对比站	0	0	0	0	0
涪陵本站	0	1.7	0	0.5	0.4
明家	0.1	0.8	0.1	0.5	0.1

地质灾害因子	分段	地灾个数（个）	信息量	信息量主要排序
坡度（°）	0~5	18	-1.514609	-
	5~10	31	-0.352688	-
	10~15	87	0.530870	11
	15~20	56	0.667521	8
	20~25	4	-1.244959	-
坡向(°)	平坦	5	-2.08588	-
	0~30	10	-0.326033	-
	30~150	66	0.189856	19
	150~200	21	-0.005354	-
	200~250	30	0.372565	16
	250~310	32	-0.020237	-
	310~330	13	0.04228	-
	330~360	19	0.357179	17
累积汇水面积（格网）	1~2	114	0.100335	21
	2~4	31	0.437477	15
	4~8	15	-0.550313	-
	8~20	21	0.162117	20
	>20	15	-0.793704	-
土地利用类型	耕地	76	0.503849	12
	林地	61	-7.919041	-
	园地	19	1.316537	2
	草地	5	-0.589446	-
	交通运输用地	2	0.743452	7
	水域及水利设施用地	11	0.478293	13
	城镇村及工矿用地	22	2.009376	1
离地表水距离（m）	0~150	97	1.218703	3
	150~250	60	1.187452	4
	250~400	21	-0.728606	-
	>400	18	-2.39345	-
地层岩性	三叠系中统雷口坡组	0	0	-
	三叠系下统嘉陵江组	12	-1.138709	-
	三叠系上统须家河组	0	0	-
	侏罗系下统珍珠冲组	0	0	-
	侏罗系中统新田沟组	0	0	-
	二迭系上统	0	0	-
	侏罗系上统蓬莱镇组	2	-3.63999	-
	侏罗系中统上沙溪庙组	106	0.783518	6
	二迭系下统	0	0	-
	三叠系下统飞仙关组	0	0	-
	志留系下统罗惹坪组	0	0	-
	侏罗系上统遂宁组	49	0.797644	5
	侏罗系中统下沙溪庙组	22	-0.309074	-
	侏罗系中下统自流井组	5	-0.961061	-
植被（NDVI）	-1~0	176	0.132877	21
	0~0.2	11	-1.164542	-
	0.2~0.4	9	0.353476	18
	0.4~1.0	0	0	-
降雨量（mm）	0.0~1.0	65	0.568849	10
	1.0~2.0	43	0.453245	14
	2.0~3.0	80	0.572047	9
	>3.0	8	-0.003276	-

易发性等级	该级别面积(km²)	占研究区面积比(%)(a)	灾害点个数	占灾害点总数比(%)(b)	灾积比（b/a）
低	449.617	15.27	3	1.5	0.09
较低	666.110	22.62	12	6.3	0.27
中	895.299	30.41	21	10.8	0.36
较高	648.251	21.97	56	28.7	1.31
高	286.582	9.73	104	52.7	5.42