基于信息熵与AHP模型的白龙江流域泥石流危险性评价
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摘要
白龙江流域是我国泥石流最为严重的地区之一,其泥石流灾害类型多,具有多发性和继承性的特点。受“5·12”汶川大地震影响,该区山体和岩土结构破坏加剧,诱发了包括舟曲“8·8”特大泥石流灾害在内的一系列新泥石流等地质灾害,严重威胁着当地人民的生命财产、重要基础设施甚至长江上游的生态安全。因此,针对白龙江流域进行泥石流灾害危险性评价与防治技术研究具有重要的现实意义。
本文在对白龙江流域的内外动力环境因子进行分析研究,建立研究区泥石流灾害评价指标体系的基础上,分别基于信息熵和AHP模型对研究区的泥石流危险性进行评价,并对评价模型结果进行对比分析,获取了以下主要成果：
1.根据野外实际考察和相关文献资料搜集所得数据,基于Geodatabase建立研究区的泥石流灾害及其相关要素空间数据库,主要包括基础资料库、地质环境空间数据库和泥石流空间数据库,为泥石流危险性评价奠定基础。
2.泥石流灾害空间分布特征：空间分布特征主要表现为分布密度大、空间异质性明显、同级流域泥石流分布高度既集中又分散、泥石流大多沿断裂带和较软弱岩性区分布等特征；
3.泥石流灾害时间分布特征主要包括年际时间分布规律、年内时间分布规律和日时段分布规律。年际时间分布规律表现为泥石流多发生在丰水年份和趋于丰水年份,而在低降水年份和趋于低降水年份泥石流不发育；年内时间分布规律表现泥石流发生时间也主要分布在6-9月,并集中在7-8月；日时段分布规律发布规律表现为泥石流多发生在午后(14时至20时)和夜间(24时至次日8时)。
4.根据泥石流的发育背景、形成条件、分布特征等因素,对各影响因子进行逐因子分析、共线性诊断和关联度计算,分别建立基于栅格单元和基于流域单元的泥石流危险性评价指标,其中基于流域单元的影响该区域泥石流危险性指标依次为：地层岩性>主沟长度>10min平均降雨量>流域相对高差>流域面积>NDVI>主沟比降>流域切割密度。
5.论文首先采用AHP模型进行泥石流危险性评价,但由于AHP方法本身的限制,评价结果有很强的主观性。为了最大程度上克服主观因素对泥石流评价结果的影响,又采用信息熵模型分别基于栅格单元和流域单元进行泥石流危险性评价,分别生产了基于栅格单元与基于流域的白龙江流域泥石流危险性评价空间分布图。结果表明两种评价单元的泥石流危险性评价结果整体相似,仅在局部存在差异,但基于流域单元的泥石流危险性评价结果与实际情况更吻合,说明流域水文地形状况对泥石流的发生具有更强的作用,在泥石流危险性评价时需采用更多水文地形指标,以使评价结果更为准确合理。
6.基于栅格单元的信息熵模型和AHP模型泥石流危险性评价结果呈现出总体趋势相似,局部略微差异的特点。两种评价结果对比分析显示：断层对研究区泥石流的危险性空间分布起到主导作用。
Bailong basin was one of the serious areas of debris flow in china, and having the characteristic of great frequency and hereditability. The structure of mountains, rock and soil are severely damaged under Wenchuan "5·12" earthquake influence, induced a serious of geological disasters included Zhouqu "8·8" Catastrophic debris flow. The serious geological disasters threatern the the life and property of the people, significant infrastructure and even to the Ecological Security in the Upper Reaches of the Yangzi River, controlling techniques and Risk assessment of debris flow in the Bailong basin were of important realistic significance.
Based on the analysis of the environmental factors of the internal and external force, establishing the symbol system, the Information Entropy and AHP model were applied to assess the risk of debris flow in study area and have a Comparative Analysis of the result of assess model. The results showed:
1. based on Geodatabas, built the space database of debris flow and its relating factors which included the underlying database, spatial database of geological environment and debris flow to develop a base for risk assessment of ebris flow in accordance with the practical investigation in the field and Related documents.
2. The results showed that the spatial distribution characteristics of debris flow of the great density distribution, conspicuous spatial heterogeneity, the notable disparity of altitude distribution of debris flow in different areas of the same catchment, debris distributed along the Fault zone and the areas of the soft and weak rock mostly.
3. The distribution feature of debris flow time mainly includeed the annual time distribution, years time distribution rule and day time distribution rule. The distribution for the annual time performanced that debris flow occurs in a wet year and tended to be in a wet year, and not yet developed in low rainfall and tended to be low rainfall of the year; The distribution rule for the years time of debris flow performance that the time debris occurred mainly distributed in June to September, and concentrated in July-August. The distribution rule for day time performance for the debris flow occurs in the afternoon (14to20) and the night (24to8at the next day).
4. According to the development background, the forming condition, the distribution and other factors of debris flow, do factor analysis, collinearity diagnostics and correlation calculation with every factor, established the assessment indicator for debris flow hazard degree separately based on grid unit and valley unit. And the impact factors of debris flow hazard degree based on valley unit are listed in turn here:formation lithology> main ditch length>10minutes average rainfall> valley relative relief> valley area> NDVI> main ditch gradient> valley cutting density.
5. In the paper, it firstly adopted AHP model to conduct the risk assessment of debris flow, but due to the limitation of the AHP method, the evaluation result has the very strong subjectivity. In order to overcome subjective factors to affect the results of evaluation of debris flow in the largest extent, and based on grid unit and watershed unit to conduct the risk assessment of debris flow using the information entropy model, which respectively produced based on grid unit and space distribution of debris flow risk assessment in Bailong Basin. The result showed that the debris risk assessment outcomes by the two methods are alike, with difference only in local regions, however, the one based on basin unit is accordant with the actual situation, indicating that hydrologic terrain condition has a strong role the occurrence of debris flow, and more hydrological terrain index must be adopted in debris flow risk assessment, in order to make the assessment more exact and reasonable.
6. Based on grid unit's information entropy model and the AHP model of debris flow risk assessment, the two results presented a general trend similar, and the characteristics of the local slightly differences. A comparative analysis of the two kinds of evaluation results showed:a fault in the study area of space debris flow risk distribution plays a leading role.
本文在对白龙江流域的内外动力环境因子进行分析研究,建立研究区泥石流灾害评价指标体系的基础上,分别基于信息熵和AHP模型对研究区的泥石流危险性进行评价,并对评价模型结果进行对比分析,获取了以下主要成果：
1.根据野外实际考察和相关文献资料搜集所得数据,基于Geodatabase建立研究区的泥石流灾害及其相关要素空间数据库,主要包括基础资料库、地质环境空间数据库和泥石流空间数据库,为泥石流危险性评价奠定基础。
2.泥石流灾害空间分布特征：空间分布特征主要表现为分布密度大、空间异质性明显、同级流域泥石流分布高度既集中又分散、泥石流大多沿断裂带和较软弱岩性区分布等特征；
3.泥石流灾害时间分布特征主要包括年际时间分布规律、年内时间分布规律和日时段分布规律。年际时间分布规律表现为泥石流多发生在丰水年份和趋于丰水年份,而在低降水年份和趋于低降水年份泥石流不发育；年内时间分布规律表现泥石流发生时间也主要分布在6-9月,并集中在7-8月；日时段分布规律发布规律表现为泥石流多发生在午后(14时至20时)和夜间(24时至次日8时)。
4.根据泥石流的发育背景、形成条件、分布特征等因素,对各影响因子进行逐因子分析、共线性诊断和关联度计算,分别建立基于栅格单元和基于流域单元的泥石流危险性评价指标,其中基于流域单元的影响该区域泥石流危险性指标依次为：地层岩性>主沟长度>10min平均降雨量>流域相对高差>流域面积>NDVI>主沟比降>流域切割密度。
5.论文首先采用AHP模型进行泥石流危险性评价,但由于AHP方法本身的限制,评价结果有很强的主观性。为了最大程度上克服主观因素对泥石流评价结果的影响,又采用信息熵模型分别基于栅格单元和流域单元进行泥石流危险性评价,分别生产了基于栅格单元与基于流域的白龙江流域泥石流危险性评价空间分布图。结果表明两种评价单元的泥石流危险性评价结果整体相似,仅在局部存在差异,但基于流域单元的泥石流危险性评价结果与实际情况更吻合,说明流域水文地形状况对泥石流的发生具有更强的作用,在泥石流危险性评价时需采用更多水文地形指标,以使评价结果更为准确合理。
6.基于栅格单元的信息熵模型和AHP模型泥石流危险性评价结果呈现出总体趋势相似,局部略微差异的特点。两种评价结果对比分析显示：断层对研究区泥石流的危险性空间分布起到主导作用。
Bailong basin was one of the serious areas of debris flow in china, and having the characteristic of great frequency and hereditability. The structure of mountains, rock and soil are severely damaged under Wenchuan "5·12" earthquake influence, induced a serious of geological disasters included Zhouqu "8·8" Catastrophic debris flow. The serious geological disasters threatern the the life and property of the people, significant infrastructure and even to the Ecological Security in the Upper Reaches of the Yangzi River, controlling techniques and Risk assessment of debris flow in the Bailong basin were of important realistic significance.
Based on the analysis of the environmental factors of the internal and external force, establishing the symbol system, the Information Entropy and AHP model were applied to assess the risk of debris flow in study area and have a Comparative Analysis of the result of assess model. The results showed:
1. based on Geodatabas, built the space database of debris flow and its relating factors which included the underlying database, spatial database of geological environment and debris flow to develop a base for risk assessment of ebris flow in accordance with the practical investigation in the field and Related documents.
2. The results showed that the spatial distribution characteristics of debris flow of the great density distribution, conspicuous spatial heterogeneity, the notable disparity of altitude distribution of debris flow in different areas of the same catchment, debris distributed along the Fault zone and the areas of the soft and weak rock mostly.
3. The distribution feature of debris flow time mainly includeed the annual time distribution, years time distribution rule and day time distribution rule. The distribution for the annual time performanced that debris flow occurs in a wet year and tended to be in a wet year, and not yet developed in low rainfall and tended to be low rainfall of the year; The distribution rule for the years time of debris flow performance that the time debris occurred mainly distributed in June to September, and concentrated in July-August. The distribution rule for day time performance for the debris flow occurs in the afternoon (14to20) and the night (24to8at the next day).
4. According to the development background, the forming condition, the distribution and other factors of debris flow, do factor analysis, collinearity diagnostics and correlation calculation with every factor, established the assessment indicator for debris flow hazard degree separately based on grid unit and valley unit. And the impact factors of debris flow hazard degree based on valley unit are listed in turn here:formation lithology> main ditch length>10minutes average rainfall> valley relative relief> valley area> NDVI> main ditch gradient> valley cutting density.
5. In the paper, it firstly adopted AHP model to conduct the risk assessment of debris flow, but due to the limitation of the AHP method, the evaluation result has the very strong subjectivity. In order to overcome subjective factors to affect the results of evaluation of debris flow in the largest extent, and based on grid unit and watershed unit to conduct the risk assessment of debris flow using the information entropy model, which respectively produced based on grid unit and space distribution of debris flow risk assessment in Bailong Basin. The result showed that the debris risk assessment outcomes by the two methods are alike, with difference only in local regions, however, the one based on basin unit is accordant with the actual situation, indicating that hydrologic terrain condition has a strong role the occurrence of debris flow, and more hydrological terrain index must be adopted in debris flow risk assessment, in order to make the assessment more exact and reasonable.
6. Based on grid unit's information entropy model and the AHP model of debris flow risk assessment, the two results presented a general trend similar, and the characteristics of the local slightly differences. A comparative analysis of the two kinds of evaluation results showed:a fault in the study area of space debris flow risk distribution plays a leading role.
引文
[1]任非凡.G212线陇南段泥石流危险性区划研究[D].兰州：兰州大学.2006.
[2]唐川.基于GIS的滇西北地区泥石流危险性评价何减灾规划研究[J]China Academic Journal Electronic Publishing House.1994-2007:237.
[3]苏强.基于DEM的黄土滑坡危险性评价研究[D].北京：中国地质大学.2006.
[4](苏)C.M.弗莱施曼著,姚德基译.泥石流[M].北京：科学出版社.1986：29-35.
[5](日)足立胜治,德山久仁夫等.土石流发生危险度判定[J].1977,30(3)：7-16.
[6]Takahashi T, Estimation of potential debris flows and their hazardous zones:Soft counter measures for a disaster[J]. JOURAL OF NATURAL DISASTER SCIENCE.1981:57-89.
[7]Olivier Lateltin, Example of hazard assessment and land-use planning in Switzerland for snow avalanches, floods and landslides[J]. Swiss national hydrological and geological survey. Bern,1998.
[8]Hungr O, Morgan C.G, Vandine F.D, etc. Debris flow defenses in British Columbia[J]. GEOLOGICAL SOCIETY OF AMERICA REVIEWS IN ENGINEERING GEOLOGY. 1987:201-222.
[9]Eldeen M.T. Predisaster Physical Planning:Inteqration of Disaster Risk Analysis into Physical Planning-A Case Study in Tunisia DISASTERS[C].1980,4(2):211-212.
[10]Hollingsworth R, GS.Kovacs. Soil slumps and debris flows:Prediction and protection[J]. Bulletin of the Association of Engineering Geologists.1981,18(1):17-28.
[11]Okimura T. A slope stability method for predicting rapid mass movements on granite mountain slopes[J]. Natural Disaster Science.1983,5(1):13-30.
[12]Peatross J.L. A morphometric study of slop stability controls in Central Virginia[D]. University of Virginia.1986.
[13]Dikau R, Cavallin A, Jager S. Databases and GIS for landslide research in Europe[J]. Geomorphology.1996,15(3-4):227-239.
[14]Walshs J, D.R Bulter. Morphometric and multispectral image analysis of debris flows for Natural hazard assessment [J]. Geoearto International.1997,12(1):59-70.
[15]Wadge G.A, P.W islocki. Special analysis in GIS for natural hazard assessment A]. Environmental Modelling with GIS[M]. Oxford:Oxford University Press.1993:332-338.
[16]闰满存.GIS支持的澜沧江下游区泥石流爆发危险性评价[J].地理科学.2001,21(4)：335-338.
[17]王礼先.北京山区荒溪分类与危险区制图[J].山地研究.1995,13(3)：141-146.
[18]谭炳炎.泥石流沟严重程度的数量化综合评判[J].水土保持通报.1986,6(1)：51-57.
[19]刘希林.泥石流危险度判定的研究[J].灾害学.1988,3(3)：10-15.
[20]魏永明,谢又予,伍永秋.关联度分析法和模糊综合评判法在泥石流沟谷危险度划分中的应用[J].自然灾害学报.1998,7(2)：109-117.
[21]唐川,刘希林,朱静.泥石流堆积泛滥区危险度的评价与应用[J].自然灾害学报.1993,2(4)：79-84.
[22]李阔,唐川.泥石流危险性评价研究进展[J].灾害学.2007,22(1)：106-111.
[23]赵源.风险评价技术在2003年“7.11”泥石流灾害评价中的应用[J].灾害学.2004, 19(1)：35-39.
[24]李新坡,莫多闻.应用GIS和神经网络方法进行泥石流危险度评价的研究[J].水土保持研究.2005,12(8)：6-9.
[25]刘洪江,韩用顺等.云南昆明东川区泥石流危险性评价[J].水土保持研究.2007,14(6)：245-249.
[26]麦华山,徐林荣等.基于超熵理论的泥石流危险性模糊综合评判[J].地质灾害与环境保护.2007,18(4)：62-66.
[27]王晓朋,潘懋,任群智.基于流域系统地貌信息熵的泥石流危险性定量评价[J].北京大学学报(自然科学版).2007,43(2)：211-215.
[28]胡浩鹏.北京市泥石流灾害风险评估指标体系及方法研究[D].北京：中国地质大学.2007.
[29]韦方强,胡凯衡.泥石流危险性分区及其在泥石流减灾中的应用[J].中国地质灾害与防治学报.2007,18(1)：23-27.
[30]韩金华.基于GIS的白龙江流域泥石流危险性评价研究[D].兰州：兰州大学.2010.
[31]王协康,敖汝庄,方铎.白龙江流域泥石流沟形态的非线性特征[J].四川水力发电.2008,19(1)：23-26.
[32]原俊红.白龙江中游滑坡堵江问题研究[D].兰州：兰州大学.2007.
[33]韩国营.甘肃省白龙江流域苔类植物的研究[D].济南：山东师范大学.2009.
[34]崔凯.拟建G212线陇南段泥石流形成条件因子的敏感性研究[J].西部探矿工程.2005,17(12)：289-291.
[35]甘肃省长江流域重点治理区水土保持领导小组办公室.长江上游滑坡泥石流预警系统调查-陇南,陕南普查报告[R].兰州：甘肃省科学院地质自然灾害防治研究所.1990.
[36]中国科学院兰州冰川冻土研究所.甘肃泥石流[M].北京：人民交通出版社.1982.
[37]刘涛.密云县泥石流危险度评价及防治措施体系研究[D].北京：北京林业大学.2009.
[38]崔鹏,杨坤,韦方强等.泥石流灾情评估指标体系[J].自然灾害学报.2001,10(4)：36-41.
[39]赵源,刘希林.泥石流灾害损失评价[J].中国地质灾害与防治学报.2005：42-53.
[40]罗元华.论泥石流灾害风险评估方法[J].中国矿业.2000,9(6)：70-72.
[41]谭万沛,王成华.暴雨泥石流滑坡的区域预测与预报[M].成都：四川科学技术出版社.1994.
[42]汤家法,谢洪.GIS技术支持下的泥石流危险度区划研究-以岷江上游为例[J].四川测绘,1999：120-122.
[43]唐川,朱静.GIS支持下的滇西北地区泥石流灾害评价[J].水土保持学报.2001,15(6)：84-87.
[44]韦方强,谢洪,钟敦伦.四川省泥石流危险度区划[J].水土保持学报.2000,14(1)：59-63.
[45]陈国玉.喜马拉雅山中部地区泥石流危险性评价研究[D].长春：吉林大学.2010.
[46]刘少峰等.数字高程模型在地表过程研究中的应用[J].地学前缘.2005,12(1)：303-309.
[47]姚晨等.地形复杂区域的典型植被指数评估[J].遥感技术与应用.2009,24(4)：496-501.
[48]刘希林,张松林,唐川.沟谷泥石流危险度评价研究[J].水土保持学报.1993：20-25.
[49]汪悦.西藏隆子镇泥石流灾害危险性评价研究[D].长春：吉林大学.2009.
[50]匡乐红.区域暴雨泥石流预测预报方法研究[D].长沙：中南大学.2007.
[51]刘希林,张松林,唐川.中国西南山区沟谷暴雨泥石流危险度判定的基本原理和方法[J].云南地理环境研究.1993,5(2)：62-70.
[52]中国科学院水利部成都山地灾害与环境研究所.泥石流研究与防治[M].成都：四川科学技术出版社.2000.
[53]Saaty T. L. The Analytic Hierarchy Process. McGraw Hill[A]. Inc.1980:22-71.
[54]徐建华.现代地理学中的数学方法[M].北京：高等教育出版社.2002.
[55]汤国安等.遥感数字图像处理[M].北京：科学出版社.2004.
[56]张梁,张业成,罗元华等.地质灾害灾情评估理论与实践[M].北京：地质出版社.1998.
[57]于秀治,韦京莲.灰色系统理论在北京山区泥石流危险度评价预测中的应用[J].自然灾害学报.1998,7(2)：109-117.
[58]蒋忠信.泥石流流域系统的超熵[J].中国地质灾害与防治学报.1992,3(1)：70-74.
[59]刘希林,唐川.泥石流危险性评价[M].北京：科学出版社.1995.
[60]Ash Robert R. Information Theory[M]. New York:Dover Publications.1990.
[61]铁永波,唐川.基于信息熵理论的泥石流沟谷危险度评价[J].灾害学.2005,12(4)：25-30.
[62]岳天详,艾南山,张英保.论流域系统稳定性的判别指标-超熵[J].水土保持学报.1989,3(2)：20-28.
[2]唐川.基于GIS的滇西北地区泥石流危险性评价何减灾规划研究[J]China Academic Journal Electronic Publishing House.1994-2007:237.
[3]苏强.基于DEM的黄土滑坡危险性评价研究[D].北京：中国地质大学.2006.
[4](苏)C.M.弗莱施曼著,姚德基译.泥石流[M].北京：科学出版社.1986：29-35.
[5](日)足立胜治,德山久仁夫等.土石流发生危险度判定[J].1977,30(3)：7-16.
[6]Takahashi T, Estimation of potential debris flows and their hazardous zones:Soft counter measures for a disaster[J]. JOURAL OF NATURAL DISASTER SCIENCE.1981:57-89.
[7]Olivier Lateltin, Example of hazard assessment and land-use planning in Switzerland for snow avalanches, floods and landslides[J]. Swiss national hydrological and geological survey. Bern,1998.
[8]Hungr O, Morgan C.G, Vandine F.D, etc. Debris flow defenses in British Columbia[J]. GEOLOGICAL SOCIETY OF AMERICA REVIEWS IN ENGINEERING GEOLOGY. 1987:201-222.
[9]Eldeen M.T. Predisaster Physical Planning:Inteqration of Disaster Risk Analysis into Physical Planning-A Case Study in Tunisia DISASTERS[C].1980,4(2):211-212.
[10]Hollingsworth R, GS.Kovacs. Soil slumps and debris flows:Prediction and protection[J]. Bulletin of the Association of Engineering Geologists.1981,18(1):17-28.
[11]Okimura T. A slope stability method for predicting rapid mass movements on granite mountain slopes[J]. Natural Disaster Science.1983,5(1):13-30.
[12]Peatross J.L. A morphometric study of slop stability controls in Central Virginia[D]. University of Virginia.1986.
[13]Dikau R, Cavallin A, Jager S. Databases and GIS for landslide research in Europe[J]. Geomorphology.1996,15(3-4):227-239.
[14]Walshs J, D.R Bulter. Morphometric and multispectral image analysis of debris flows for Natural hazard assessment [J]. Geoearto International.1997,12(1):59-70.
[15]Wadge G.A, P.W islocki. Special analysis in GIS for natural hazard assessment A]. Environmental Modelling with GIS[M]. Oxford:Oxford University Press.1993:332-338.
[16]闰满存.GIS支持的澜沧江下游区泥石流爆发危险性评价[J].地理科学.2001,21(4)：335-338.
[17]王礼先.北京山区荒溪分类与危险区制图[J].山地研究.1995,13(3)：141-146.
[18]谭炳炎.泥石流沟严重程度的数量化综合评判[J].水土保持通报.1986,6(1)：51-57.
[19]刘希林.泥石流危险度判定的研究[J].灾害学.1988,3(3)：10-15.
[20]魏永明,谢又予,伍永秋.关联度分析法和模糊综合评判法在泥石流沟谷危险度划分中的应用[J].自然灾害学报.1998,7(2)：109-117.
[21]唐川,刘希林,朱静.泥石流堆积泛滥区危险度的评价与应用[J].自然灾害学报.1993,2(4)：79-84.
[22]李阔,唐川.泥石流危险性评价研究进展[J].灾害学.2007,22(1)：106-111.
[23]赵源.风险评价技术在2003年“7.11”泥石流灾害评价中的应用[J].灾害学.2004, 19(1)：35-39.
[24]李新坡,莫多闻.应用GIS和神经网络方法进行泥石流危险度评价的研究[J].水土保持研究.2005,12(8)：6-9.
[25]刘洪江,韩用顺等.云南昆明东川区泥石流危险性评价[J].水土保持研究.2007,14(6)：245-249.
[26]麦华山,徐林荣等.基于超熵理论的泥石流危险性模糊综合评判[J].地质灾害与环境保护.2007,18(4)：62-66.
[27]王晓朋,潘懋,任群智.基于流域系统地貌信息熵的泥石流危险性定量评价[J].北京大学学报(自然科学版).2007,43(2)：211-215.
[28]胡浩鹏.北京市泥石流灾害风险评估指标体系及方法研究[D].北京：中国地质大学.2007.
[29]韦方强,胡凯衡.泥石流危险性分区及其在泥石流减灾中的应用[J].中国地质灾害与防治学报.2007,18(1)：23-27.
[30]韩金华.基于GIS的白龙江流域泥石流危险性评价研究[D].兰州：兰州大学.2010.
[31]王协康,敖汝庄,方铎.白龙江流域泥石流沟形态的非线性特征[J].四川水力发电.2008,19(1)：23-26.
[32]原俊红.白龙江中游滑坡堵江问题研究[D].兰州：兰州大学.2007.
[33]韩国营.甘肃省白龙江流域苔类植物的研究[D].济南：山东师范大学.2009.
[34]崔凯.拟建G212线陇南段泥石流形成条件因子的敏感性研究[J].西部探矿工程.2005,17(12)：289-291.
[35]甘肃省长江流域重点治理区水土保持领导小组办公室.长江上游滑坡泥石流预警系统调查-陇南,陕南普查报告[R].兰州：甘肃省科学院地质自然灾害防治研究所.1990.
[36]中国科学院兰州冰川冻土研究所.甘肃泥石流[M].北京：人民交通出版社.1982.
[37]刘涛.密云县泥石流危险度评价及防治措施体系研究[D].北京：北京林业大学.2009.
[38]崔鹏,杨坤,韦方强等.泥石流灾情评估指标体系[J].自然灾害学报.2001,10(4)：36-41.
[39]赵源,刘希林.泥石流灾害损失评价[J].中国地质灾害与防治学报.2005：42-53.
[40]罗元华.论泥石流灾害风险评估方法[J].中国矿业.2000,9(6)：70-72.
[41]谭万沛,王成华.暴雨泥石流滑坡的区域预测与预报[M].成都：四川科学技术出版社.1994.
[42]汤家法,谢洪.GIS技术支持下的泥石流危险度区划研究-以岷江上游为例[J].四川测绘,1999：120-122.
[43]唐川,朱静.GIS支持下的滇西北地区泥石流灾害评价[J].水土保持学报.2001,15(6)：84-87.
[44]韦方强,谢洪,钟敦伦.四川省泥石流危险度区划[J].水土保持学报.2000,14(1)：59-63.
[45]陈国玉.喜马拉雅山中部地区泥石流危险性评价研究[D].长春：吉林大学.2010.
[46]刘少峰等.数字高程模型在地表过程研究中的应用[J].地学前缘.2005,12(1)：303-309.
[47]姚晨等.地形复杂区域的典型植被指数评估[J].遥感技术与应用.2009,24(4)：496-501.
[48]刘希林,张松林,唐川.沟谷泥石流危险度评价研究[J].水土保持学报.1993：20-25.
[49]汪悦.西藏隆子镇泥石流灾害危险性评价研究[D].长春：吉林大学.2009.
[50]匡乐红.区域暴雨泥石流预测预报方法研究[D].长沙：中南大学.2007.
[51]刘希林,张松林,唐川.中国西南山区沟谷暴雨泥石流危险度判定的基本原理和方法[J].云南地理环境研究.1993,5(2)：62-70.
[52]中国科学院水利部成都山地灾害与环境研究所.泥石流研究与防治[M].成都：四川科学技术出版社.2000.
[53]Saaty T. L. The Analytic Hierarchy Process. McGraw Hill[A]. Inc.1980:22-71.
[54]徐建华.现代地理学中的数学方法[M].北京：高等教育出版社.2002.
[55]汤国安等.遥感数字图像处理[M].北京：科学出版社.2004.
[56]张梁,张业成,罗元华等.地质灾害灾情评估理论与实践[M].北京：地质出版社.1998.
[57]于秀治,韦京莲.灰色系统理论在北京山区泥石流危险度评价预测中的应用[J].自然灾害学报.1998,7(2)：109-117.
[58]蒋忠信.泥石流流域系统的超熵[J].中国地质灾害与防治学报.1992,3(1)：70-74.
[59]刘希林,唐川.泥石流危险性评价[M].北京：科学出版社.1995.
[60]Ash Robert R. Information Theory[M]. New York:Dover Publications.1990.
[61]铁永波,唐川.基于信息熵理论的泥石流沟谷危险度评价[J].灾害学.2005,12(4)：25-30.
[62]岳天详,艾南山,张英保.论流域系统稳定性的判别指标-超熵[J].水土保持学报.1989,3(2)：20-28.