太白县地质灾害隐患点危险性评价
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摘要
太白县地质灾害较严重,其灾点类型多、成片成带分布,规模差异较大,影响和受控因素多,发生频率较高,小型及中型灾点多,其中危害属重大级的灾点较少;由于其复杂的地质条件、地形地貌条件、气象水文条件和强烈的新构造运动及人为工程活动,使得本区成为地质灾害多发县区之一。
本文结合2009年“陕西省太白县地质灾害详细调查”任务,对太白县地质灾害详细调查及室内试验等工作的基础上,总结了区内滑坡隐患点的发育特征及分布规律;并对区内滑坡灾害点进行了危险性评价,研究的主要内容有:
(1)分析了区内滑坡隐患点的类型、规模、发育特征及形成条件等;在对滑坡隐患点调查资料进行分析研究的基础上,根据区内滑坡隐患点的特征,以规模、稳定程度、威胁人数、威胁财产数四个危险性制约因子运用层次分析法构造训练样本集。
(2)应用MATLAB工具箱,调用newff()函数建立神经网络,对动量BP算法与SCG算法的训练结果进行比较,确定SCG算法为训练方法。
(3)应用LibSVM工具箱来实现支持向量机模型,通过对多项式核函数、径向基核函数RBF和Sigmoid核函数的样本训练结果及误差分析确定了径向基RBF为模型核函数。
应用两种方法计算研究区内81处滑坡隐患点危险性指数,并对结果进行分析比较,与以往隐患点危险性定性评价结果基本一致,本研究成果对于太白县地质灾害隐患点防治及人工智能应用于地质灾害隐患点危险性评价具有指导意义。
Taibai more serious geological disasters, the disaster point of many types and dist-ribution into the film into a zone, quite different scale, impact and controlled by man-y factors, high frequency, small and medium-sized disaster points, which are significant to level against disaster points less; because of its complex geological conditions, ter-rain conditions, meteorological and hydrological conditions and a strong new tectonic movement and human engineering activities, making the area into one of geological disasters counties.
In this paper, 2009 "Shaanxi Taibai detailed investigation of geological disasters, " the task of Taibai geological disasters such as a detailed investigation and laboratory test based on the work, summed up the landslide hazard area and development characteristics of point distribution; and the region Point landslide risk assessment carried out to study the main contents are:
(1) Analysis of the regional landslide hazard point type, size, development characteristics and formation conditions; landslide hazards in the analysis of survey data point-s based on the study, according to the characteristics of regional landslide hazard poin-ts to the scale, stability , the number of threats, threats to property, restricting the nu-mber of four risk factors AHP construct the training sample set.
(2) application of MATLAB toolbox, called newff () function of neural networks, BP algorithm with momentum SCG training algorithm results were compared to deter-mine the SCG algorithm for training methods.
(3) application LibSVM toolbox to implement support vector machine model, by t-he polynomial kernel function, radial basis function RBF and Sigmoid kernel function of the sample training results and error analysis to determine the radial basis kernel function RBF as a model.
Application of two methods in the study area 81 point landslide hazard risk inde-x, and the results were analyzed and compared with the previous qualitative assessment of risk of hidden point was consistent, the results of this study point to Taibai risks of geological disasters prevention and artificial intelligence Applied to geological disa-sters have hidden point risk assessment guidance.
本文结合2009年“陕西省太白县地质灾害详细调查”任务,对太白县地质灾害详细调查及室内试验等工作的基础上,总结了区内滑坡隐患点的发育特征及分布规律;并对区内滑坡灾害点进行了危险性评价,研究的主要内容有:
(1)分析了区内滑坡隐患点的类型、规模、发育特征及形成条件等;在对滑坡隐患点调查资料进行分析研究的基础上,根据区内滑坡隐患点的特征,以规模、稳定程度、威胁人数、威胁财产数四个危险性制约因子运用层次分析法构造训练样本集。
(2)应用MATLAB工具箱,调用newff()函数建立神经网络,对动量BP算法与SCG算法的训练结果进行比较,确定SCG算法为训练方法。
(3)应用LibSVM工具箱来实现支持向量机模型,通过对多项式核函数、径向基核函数RBF和Sigmoid核函数的样本训练结果及误差分析确定了径向基RBF为模型核函数。
应用两种方法计算研究区内81处滑坡隐患点危险性指数,并对结果进行分析比较,与以往隐患点危险性定性评价结果基本一致,本研究成果对于太白县地质灾害隐患点防治及人工智能应用于地质灾害隐患点危险性评价具有指导意义。
Taibai more serious geological disasters, the disaster point of many types and dist-ribution into the film into a zone, quite different scale, impact and controlled by man-y factors, high frequency, small and medium-sized disaster points, which are significant to level against disaster points less; because of its complex geological conditions, ter-rain conditions, meteorological and hydrological conditions and a strong new tectonic movement and human engineering activities, making the area into one of geological disasters counties.
In this paper, 2009 "Shaanxi Taibai detailed investigation of geological disasters, " the task of Taibai geological disasters such as a detailed investigation and laboratory test based on the work, summed up the landslide hazard area and development characteristics of point distribution; and the region Point landslide risk assessment carried out to study the main contents are:
(1) Analysis of the regional landslide hazard point type, size, development characteristics and formation conditions; landslide hazards in the analysis of survey data point-s based on the study, according to the characteristics of regional landslide hazard poin-ts to the scale, stability , the number of threats, threats to property, restricting the nu-mber of four risk factors AHP construct the training sample set.
(2) application of MATLAB toolbox, called newff () function of neural networks, BP algorithm with momentum SCG training algorithm results were compared to deter-mine the SCG algorithm for training methods.
(3) application LibSVM toolbox to implement support vector machine model, by t-he polynomial kernel function, radial basis function RBF and Sigmoid kernel function of the sample training results and error analysis to determine the radial basis kernel function RBF as a model.
Application of two methods in the study area 81 point landslide hazard risk inde-x, and the results were analyzed and compared with the previous qualitative assessment of risk of hidden point was consistent, the results of this study point to Taibai risks of geological disasters prevention and artificial intelligence Applied to geological disa-sters have hidden point risk assessment guidance.
引文
[1]乔小艳,杨军.关于地质灾害危险性评估工作方法及现状简介[J].科技资讯,2009,5:205.
[2]成玉祥,任春林等.基于BT神经网络的地质灾害危险性评估方法探讨[J].中国地质灾害与防治学报,2008,6(2):100—101.
[3]李莎莎,王得楷等.积分值法在甘肃农村小城镇地质灾害评估中的应用[J].安徽农业科学,2009,2(14):6501—6502.
[4]陕西省宝鸡市太白县聚仙山滑坡隐患点应急治理工程勘查/设计报告[R].中国有色金属工业西安勘察设计研究院,2008.
[5]王思敬.中国城市发展中的地质环境问题.第四纪研究,2003.
[6]殷跃平,柳源.中国地质灾害防治研究—对国际减灾十年活动(1990—2000年)的思索[J].工程地质学报,2000,增刊:1-10.
[7] R.P.Gupta, B.C.Joshi. Landslide hazard zoning using the GIS approach—A case study from the Ramganga catchment, Himalayas[J]. Engineering Geology,1990,28:119-131.
[8] Fausto Guzzetti. Landslide fatalities and the evaluation of landslide risk in Italy[J]. Engi-neering Geology,2000,58(2):89-107.
[9] Piyoosh Rautelal, Ramesh Chandra Lakhera. Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya(India)[J]. ITC Journal/the International Institute for Aerial Survey and Earth Sciences,2000,2(3～4):153-160.
[10] G.C.Ohlmacher, J.C.Davis. Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas,USA[J]. Engineering Geology,2003,69: 331-343.
[11] R.Anbalagan, B.Singh. Landslide hazard and risk assessment maping of mountainous terrains—a case study from kumaun himalaya[J]. Engineering Geology,1996,43:237- 246.
[12] P.J.Finlay,R.Fell. Landslides:Risk perception and acceptance[J].Canadian Geotechnical Journal,1997,34(2):169-188.
[13] P.J.Finlay,G.R.Mostyn,R.Fell. Landslide risk assessment:prediction of travel distance[J]. Canadian Geotechnical Journal,1999,36(3):556-562.
[14] S.Lee, K.Min. Statistical analysis of landslide susceptibility at Yongin, Korea[J]. Envi-ronmental Geology,2001,40:1095-1113.
[15] F.C.Dai, D.F.Lee. Landslide characteristics and slope instability modeling using GIS,Lantau Island,Hong Kong[J]. Geomorphology,2002,48:213-228.
[16] M.L.Lin, C.C.Tung. A GIS-based potential analysis of the landslides induced by the Chi-Chi earthquake[J]. Engineering Geology,2003,64:63-77.
[17]柳源.中国地质灾害(以崩、滑、流为主)危险性分析与区划[J].中国地质灾害与防治学报,2003,14(1):95-99.
[18]张苏平,王兰民,马尔曼.GIS技术和神经网络模型在地震地质灾害小区划中的应用研究[J].西北地震学报,2003,25(4):332-334.
[19]朱兴贤,于军,武建强.基于ANN的苏锡常地裂缝预测研究[J].地质灾害与防治学报,2006,17(2):28-32.
[20]张茂省,雷学武,校培喜,等.遥感技术在黄土高原区地质灾害详细调查中的应用[J].西北地质,2007,40(3):92-97.
[21]刘彦花,吴湘滨等.基于GIS的建设项目地质灾害危险性评估[J].中国地质灾害与防治学报,2008,9(3):76—78.
[22]成玉祥,仁春林,张俊.基于BP神经网络的地质灾害危险性方法探讨——以天水地区为例[J].中国地质灾害与防治学报,2008,19(2).
[23]王小平,王芳.基于WebGIS的区域地质灾害灾情动态评估系统的实现技术研究[J].资源与环境工程,2009,4(2):175—177.
[24]刘勇键,张丽娟等.可拓聚类预测方法在地质灾害危险性评价中的应用[J].地质灾害与环境保护,2009,9(3):109—110.
[25]赵向辉等.神经网络集成学习在地质灾害危险度区划中的应用研究[J].四川大学学报,2010,42(1):51-52.
[26]蒋宗礼.人工神经网络导论.高等教育出版社,2001.8.
[27]尹朝庆,尹皓.人工智能与专家系统.中国水利水电出版社,2001.12.
[28] Bethany MY Chan,Francis YL Chin,Chung-Keung Poon.Optimal simulation of full bi-nary trees on Faulty hypercubes.IEEE Tran.On Parallel Distributed Sys-tem,1995.269-286.
[29] Philip D.Wasserman.Neural Computing:Theory and Practice,Van Nostrand Rein-hold,1989.
[30]R Hecht.Application of counter propagation networks. Neural Nerworks,1998.
[31]董长虹.Matlab神经网络与应用[M].国防工业出版社,2007.
[32]焦李成.神经网络的应用于实现[M].西安电子科技大学出版社,1995.
[33]胡守仁.神经网络导论[M].北京:科学出版社,1994.
[34]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.
[35]刘卫国.MATLAB程序设计教程[M].北京:中国水利水电出版社,2005.
[36]周开利,邓春晖.MATLAB基础及其应用教程[M].北京:北京大学出版社,2007.
[37]李铁锋等.灾害地质学[M],北京:北京大学出版社,2002.
[38]李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计.2005,26(2):28—30
[39] Steve Gunn,Support vector machines for classification and regression,ISIS. Sou-thamp·ton University.1 998
[40] A.Smola,Regression Estimation wtll Support Vector Learning Machines,Master’S Thesis,Technische Universita Munchen,Germany,1996.
[41] Zhao Dengfu,Wang Meng,Zhang Jiangshe.A Support Vector Machine Approach forShort·term Load Forecasting[J].Proceedings ofthe CSEE,2002,22(4):26-30.
[2]成玉祥,任春林等.基于BT神经网络的地质灾害危险性评估方法探讨[J].中国地质灾害与防治学报,2008,6(2):100—101.
[3]李莎莎,王得楷等.积分值法在甘肃农村小城镇地质灾害评估中的应用[J].安徽农业科学,2009,2(14):6501—6502.
[4]陕西省宝鸡市太白县聚仙山滑坡隐患点应急治理工程勘查/设计报告[R].中国有色金属工业西安勘察设计研究院,2008.
[5]王思敬.中国城市发展中的地质环境问题.第四纪研究,2003.
[6]殷跃平,柳源.中国地质灾害防治研究—对国际减灾十年活动(1990—2000年)的思索[J].工程地质学报,2000,增刊:1-10.
[7] R.P.Gupta, B.C.Joshi. Landslide hazard zoning using the GIS approach—A case study from the Ramganga catchment, Himalayas[J]. Engineering Geology,1990,28:119-131.
[8] Fausto Guzzetti. Landslide fatalities and the evaluation of landslide risk in Italy[J]. Engi-neering Geology,2000,58(2):89-107.
[9] Piyoosh Rautelal, Ramesh Chandra Lakhera. Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya(India)[J]. ITC Journal/the International Institute for Aerial Survey and Earth Sciences,2000,2(3～4):153-160.
[10] G.C.Ohlmacher, J.C.Davis. Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas,USA[J]. Engineering Geology,2003,69: 331-343.
[11] R.Anbalagan, B.Singh. Landslide hazard and risk assessment maping of mountainous terrains—a case study from kumaun himalaya[J]. Engineering Geology,1996,43:237- 246.
[12] P.J.Finlay,R.Fell. Landslides:Risk perception and acceptance[J].Canadian Geotechnical Journal,1997,34(2):169-188.
[13] P.J.Finlay,G.R.Mostyn,R.Fell. Landslide risk assessment:prediction of travel distance[J]. Canadian Geotechnical Journal,1999,36(3):556-562.
[14] S.Lee, K.Min. Statistical analysis of landslide susceptibility at Yongin, Korea[J]. Envi-ronmental Geology,2001,40:1095-1113.
[15] F.C.Dai, D.F.Lee. Landslide characteristics and slope instability modeling using GIS,Lantau Island,Hong Kong[J]. Geomorphology,2002,48:213-228.
[16] M.L.Lin, C.C.Tung. A GIS-based potential analysis of the landslides induced by the Chi-Chi earthquake[J]. Engineering Geology,2003,64:63-77.
[17]柳源.中国地质灾害(以崩、滑、流为主)危险性分析与区划[J].中国地质灾害与防治学报,2003,14(1):95-99.
[18]张苏平,王兰民,马尔曼.GIS技术和神经网络模型在地震地质灾害小区划中的应用研究[J].西北地震学报,2003,25(4):332-334.
[19]朱兴贤,于军,武建强.基于ANN的苏锡常地裂缝预测研究[J].地质灾害与防治学报,2006,17(2):28-32.
[20]张茂省,雷学武,校培喜,等.遥感技术在黄土高原区地质灾害详细调查中的应用[J].西北地质,2007,40(3):92-97.
[21]刘彦花,吴湘滨等.基于GIS的建设项目地质灾害危险性评估[J].中国地质灾害与防治学报,2008,9(3):76—78.
[22]成玉祥,仁春林,张俊.基于BP神经网络的地质灾害危险性方法探讨——以天水地区为例[J].中国地质灾害与防治学报,2008,19(2).
[23]王小平,王芳.基于WebGIS的区域地质灾害灾情动态评估系统的实现技术研究[J].资源与环境工程,2009,4(2):175—177.
[24]刘勇键,张丽娟等.可拓聚类预测方法在地质灾害危险性评价中的应用[J].地质灾害与环境保护,2009,9(3):109—110.
[25]赵向辉等.神经网络集成学习在地质灾害危险度区划中的应用研究[J].四川大学学报,2010,42(1):51-52.
[26]蒋宗礼.人工神经网络导论.高等教育出版社,2001.8.
[27]尹朝庆,尹皓.人工智能与专家系统.中国水利水电出版社,2001.12.
[28] Bethany MY Chan,Francis YL Chin,Chung-Keung Poon.Optimal simulation of full bi-nary trees on Faulty hypercubes.IEEE Tran.On Parallel Distributed Sys-tem,1995.269-286.
[29] Philip D.Wasserman.Neural Computing:Theory and Practice,Van Nostrand Rein-hold,1989.
[30]R Hecht.Application of counter propagation networks. Neural Nerworks,1998.
[31]董长虹.Matlab神经网络与应用[M].国防工业出版社,2007.
[32]焦李成.神经网络的应用于实现[M].西安电子科技大学出版社,1995.
[33]胡守仁.神经网络导论[M].北京:科学出版社,1994.
[34]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.
[35]刘卫国.MATLAB程序设计教程[M].北京:中国水利水电出版社,2005.
[36]周开利,邓春晖.MATLAB基础及其应用教程[M].北京:北京大学出版社,2007.
[37]李铁锋等.灾害地质学[M],北京:北京大学出版社,2002.
[38]李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计.2005,26(2):28—30
[39] Steve Gunn,Support vector machines for classification and regression,ISIS. Sou-thamp·ton University.1 998
[40] A.Smola,Regression Estimation wtll Support Vector Learning Machines,Master’S Thesis,Technische Universita Munchen,Germany,1996.
[41] Zhao Dengfu,Wang Meng,Zhang Jiangshe.A Support Vector Machine Approach forShort·term Load Forecasting[J].Proceedings ofthe CSEE,2002,22(4):26-30.