基于确定性系数模型的泸水市泥石流易发性评价
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摘要
以泥石流灾害频发的怒江州泸水市为研究区,选取坡度、高程、河流距离、道路距离、岩性、断裂带距离、降雨量、NDVI、土地利用9个影响因子,建立了泸水市泥石流灾害易发性评价体系。采用确定性系数模型(CF模型)进行了泥石流灾害易发性评价,将泥石流易发性评价结果分为极高易发区、高易发区、中易发区、低易发区4个等级。结果表明:研究区高易发区及以上级别的易发区内发生的泥石流灾害数量占总数的95.32%,易发性评价结果正确率达到81.99%,表明选取的评价指标合理,CF模型适用于泸水市的泥石流灾害易发性评价研究。泸水市泥石流灾害高易发区也是居民点集中分布区,评价分区结果对泸水市的泥石流灾害防灾减灾具有一定的借鉴作用。
Lushui City, Nujiang State was taken as the study area, where debris flow occurs frequently. Nine impact factors including slope, elevation, river, road, lithology, fault, precipitation, NDVI and land use were selected to build the susceptibility assessment system. The certain factor model(CF model) was used to evaluate the susceptibility, result of which can be divided into 4 areas of extremely high susceptibility, high susceptibility, middle susceptibility and low susceptibility. The results showed that the events of debris flow occurred in the area which had a susceptibility above high accounted for 95.32% of the total debris flow events, the correct rate of susceptibility evaluation results reached up to 81.99%, indicating that selected indices were reasonable and CF model was practicable in susceptibility assessment. The high susceptibility area of debris flow in Lushui City was also the distribution area where settlements were concentrated. Therefore, the evaluation results could provide the reference for debris flow disaster prevention and mitigation in Lushui City.
Lushui City, Nujiang State was taken as the study area, where debris flow occurs frequently. Nine impact factors including slope, elevation, river, road, lithology, fault, precipitation, NDVI and land use were selected to build the susceptibility assessment system. The certain factor model(CF model) was used to evaluate the susceptibility, result of which can be divided into 4 areas of extremely high susceptibility, high susceptibility, middle susceptibility and low susceptibility. The results showed that the events of debris flow occurred in the area which had a susceptibility above high accounted for 95.32% of the total debris flow events, the correct rate of susceptibility evaluation results reached up to 81.99%, indicating that selected indices were reasonable and CF model was practicable in susceptibility assessment. The high susceptibility area of debris flow in Lushui City was also the distribution area where settlements were concentrated. Therefore, the evaluation results could provide the reference for debris flow disaster prevention and mitigation in Lushui City.
引文
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[2] 吴赛儿,陈剑,Zhou Wendy,等.基于逻辑回归模型的泥石流易发性评价与检验:以金沙江上游奔子栏—昌波河段为例[J],现代地质,2018,32(3):611-622.
[3] 谭玉敏,郭栋,白冰心,等,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.
[4] 刘林通,孟兴民,郭鹏,等.基于流域单元和信息量法的白龙江流域泥石流危险性评价[J].兰州大学学报:自然科学版,2017,53(3):292-298,308.
[5] 岳溪柳,黄玫,徐庆勇,等.贵州省喀斯特地区泥石流灾害易发性评价[J].地球信息科学学报,2015,17(11):1395-1403.
[6] 张以晨,秦胜伍,翟健健,等.基于信息量的长白山地区泥石流易发性评价[J].水文地质工程地质,2018,45(2):150-158.
[7] 郭瑞,马富存,郭一兵,等.基于层次分析法的泥石流易发性评价[J].东华理工大学学报:自然科学版,2016,39(4):347-351.
[8] 付奇,何政伟,薛东剑.层次分析法在炉霍县泥石流易发性评价中的应用[J].地理空间信息,2012,10(6):139-141.
[9] 丁明涛,韦方强,王欢,等.基于聚类分析的三江并流区泥石流危险性评价[J].资源科学,2012,34(7):1257-1265.
[10] 胡凯衡,崔鹏,韩用顺,等.基于聚类和最大似然法的汶川灾区泥石流滑坡易发性评价[J].中国水土保持科学,2012,10(1):12-18.
[11] Pradhan B,Lee S.Delineation of landslide hazard areas on Penang Island,Malaysia,by using frequency ratio,logistic regression,and artificial neural network models[J].Environmental Earth Sciences,2010,60(5):1037-1054.
[12] Sdao F,Lioi D S,Pascale S,et al.Landslide susceptibility assessment by using a neuro-fuzzy model:A case study in the rupestrian heritage rich area of Matera[J].Natural Hazards and Earth System Sciences,2013,13(2):395-407.
[13] 焦方谦,赵新生,陈川.证据权模型在泥石流灾害易发性评价中的应用[J].干旱区地理,2013,36(6):1111-1124.
[14] Jiang W,Rao P,Cao R,et al.Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation[J].Journal of Geographical Sciences,2017,27(4):439-462.
[15] 刘艳辉,刘传正,唐灿,等.基于确定性系数模型的地质灾害多因子权重计算方法[J].中国地质灾害与防治学报,2015,26(1):92-97.
[16] 刘明学,陈祥,杨珊妮.基于逻辑回归模型和确定性系数的崩滑流危险性区划[J].工程地质学报,2014,22(6):1250-1256.
[17] 冯杭建,周爱国,唐小明,等.基于确定性系数的降雨型滑坡影响因子敏感性分析[J].工程地质学报,2017,25(2):436-446.
[18] 万石云,赵宁坤,段玮,等.云南省滑坡泥石流与强降水的相关性和多时间尺度特征研究[J].灾害学,2015(1):45-50.
[19] 李益敏,刘素红,李小文.基于GIS的怒江峡谷人居环境容量评价:泸水县为例[J].地理科学进展,2010,29(5):572-578.
[20] Shortliffe E H,Buchanan B G.A model of inexact reasoning in medicine[J].Mathematical Biosciences,1975,23(3/4):351-379.
[21] Heckerman D.Probabilistic Interpretations for MYCIN′s Certainty Factors[M]//Readings in uncertain Reasoning.Morgan Kaufmann Publishers Inc.1990.
[22] 许冲,戴福初,徐锡伟.基于GIS平台与证据权的地震滑坡易发性评价[J].地球科学:中国地质大学学报,2011,36(6):1155-1164.
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