基于隐患点详查及非线性叠加算法的地质灾害风险区划
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摘要
针对地质灾害风险评价中存在的隐患点与风险级别不一致、易损性评价指标单一等问题,提出了基于隐患点详查及非线性叠加算法的地质灾害风险区划模型。模型通过针对性的隐患点调查,收集隐患点数据,从中选取与地质灾害风险相关的指标参数,形成隐患点模块H,遴选地质灾害的主要影响因素建立影响因素模块T。在对研究区域进行网格划分的基础上,进一步通过密度比、归一化算法、AHP等分析方法得出网格区域内两组模块的指标评分标准及指标间权重,得出H、T值。将H、T评分值代入模型R=HαT计算得出网格的最终风险数值。根据网格坐标,生成DEM数据,并形成区域的风险区划图件。通过对比,建立的风险区划模型较传统模型具有较高的准确率和较低的错误率,具有较好的实用性和科学性。
There are two problems in the past geological disaster risk zoning: the inconsistency between the potential points of geo-disasters and the level of risk,and the over-simplicity of vulnerability assessment indicators.Focusing on these problems,this study established a model of geological disaster risk zoning based on the survey on potential points of geo-disasters and the nonlinear superposition algorithm. Through the targeted survey on potential points,the data related to the geological disaster risk index are collected to establish the independent module of potential points H. In addition,according to the influence factors of geological disasters induced by geological environments,the module of influence factors T is also established. On the basis of the meshing of the area studied,and through the analysis methods such as density ratio,normalization algorithm,AHP,the weight and standard of evaluation of the indexes of the two moduli in the grid are further obtained,which yields the values of H and T.Then the final value at risk can be calculated by introducing H and T into R = HαT. The DEM data are created according to the coordinate of the grid and the regional risk zoning map can be drawn. Compared with the traditional linear superposition model,the introduced model has higher accuracy and lower error rate,which is a practical and scientific approach.
There are two problems in the past geological disaster risk zoning: the inconsistency between the potential points of geo-disasters and the level of risk,and the over-simplicity of vulnerability assessment indicators.Focusing on these problems,this study established a model of geological disaster risk zoning based on the survey on potential points of geo-disasters and the nonlinear superposition algorithm. Through the targeted survey on potential points,the data related to the geological disaster risk index are collected to establish the independent module of potential points H. In addition,according to the influence factors of geological disasters induced by geological environments,the module of influence factors T is also established. On the basis of the meshing of the area studied,and through the analysis methods such as density ratio,normalization algorithm,AHP,the weight and standard of evaluation of the indexes of the two moduli in the grid are further obtained,which yields the values of H and T.Then the final value at risk can be calculated by introducing H and T into R = HαT. The DEM data are created according to the coordinate of the grid and the regional risk zoning map can be drawn. Compared with the traditional linear superposition model,the introduced model has higher accuracy and lower error rate,which is a practical and scientific approach.
引文
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[9]高华喜.滑坡灾害风险区划与预测研究综述[J].灾害学,2010,25(2):124-128.
[10]沈玲玲,刘连友,许冲,等.基于多模型的滑坡易发性评价—以甘肃岷县地震滑坡为例[J].工程地质学报,2016,24(1):19-28.
[11] Bui D T,Pradhan B,Lofman O,et al. Spatial prediction of landslide hazards in Hoa Binh province(Vietnam):a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models[J]. Catena,2012,96(2):28-40.
[12] Sharma L,Patel N,Ghose M,et al. Synergistic application of fuzzy logic and geo-informatics for landslide vulnerability zonation—a case study in Sikkim Himalayas,India[J]. Applied Geomatics,2013,5(4):271-284.
[13] BAI S,WANG J,ZHANG Z,et al. Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin,China[J]. Catena,2012,99(4):18-25.
[14]姚翔龙,赵法锁,王雁林,等.陕南秦巴山区地质灾害隐患调查识别探讨—以柞水县为例[J].灾害学,2016,31(3):230-234.
[2]蔡向阳,铁永波.基于CVM的地质灾害风险价值评估方法探索[J].灾害学,2017,32(1):212-215,227
[3]孟庆华,孙炜锋,王涛.陕西凤县地质灾害易发性评价研究[J].工程地质学报,2011,19(3):388-396.
[4]俞布,潘文卓,宋健,等.杭州市滑坡地质灾害危险性区划与评价[J].岩土力学,2012,33(1).
[5]王雁林,郝俊卿,赵法锁,等.汶川地震陕西重灾区地质灾害风险区划探讨[J].灾害学,2011,26(4):35-39.
[6]郭富赟,孟兴民,张永军,等.甘肃山区城镇地质灾害风险区划技术方法探讨[J].兰州大学学报(自然科学版),2014,50(5):604-610.
[7]霍艾迪,张骏,卢玉东.地质灾害易发性评价单元划分方法—以陕西省黄陵县为例[J].吉林大学学报(地球科学版),2011,41(2):523-535.
[8]刘亮亮,谢菲.基于GIS的地质灾害风险区划评价—以维西傈僳族自治县为例[J].地质灾害与环境保护,2016,27(1):37-43.
[9]高华喜.滑坡灾害风险区划与预测研究综述[J].灾害学,2010,25(2):124-128.
[10]沈玲玲,刘连友,许冲,等.基于多模型的滑坡易发性评价—以甘肃岷县地震滑坡为例[J].工程地质学报,2016,24(1):19-28.
[11] Bui D T,Pradhan B,Lofman O,et al. Spatial prediction of landslide hazards in Hoa Binh province(Vietnam):a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models[J]. Catena,2012,96(2):28-40.
[12] Sharma L,Patel N,Ghose M,et al. Synergistic application of fuzzy logic and geo-informatics for landslide vulnerability zonation—a case study in Sikkim Himalayas,India[J]. Applied Geomatics,2013,5(4):271-284.
[13] BAI S,WANG J,ZHANG Z,et al. Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin,China[J]. Catena,2012,99(4):18-25.
[14]姚翔龙,赵法锁,王雁林,等.陕南秦巴山区地质灾害隐患调查识别探讨—以柞水县为例[J].灾害学,2016,31(3):230-234.
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