基于GIS的贵州省滑坡地质灾害易发性多模型综合评价
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摘要
滑坡是世界范围内发生最多的地质灾害,近年来我国山体滑坡事故频发,且规模巨大,对人身和财产安全造成极大威胁。基于GIS的滑坡易发性区划图是现有和潜在滑坡风险的有效评估工具,可用于土地利用和基础设施规划以及发展早期预警系统。本文以贵州省为研究区域,在GIS环境下,结合历史滑坡分布情况,选取高程、坡度、岩性等10个滑坡致灾因子,应用相关区划模型,对潜在危险区进行分区分类,形成滑坡易发性区划图,从而指导土地规划,以期把滑坡造成的损失降到最低程度。
本论文将主客观线性加权模型(WLC)、Logistics回归模型(LR)、自组织特征映射神经网络模型(SOM),引入滑坡灾害敏感性评估中,主要完成了以下工作：
(1)根据历史滑坡的面积密度,以信息熵权理论计算一级指标的客观权重,应用梯形模糊数加权(TFNW)方法计算二级指标的主观权重,综合主客观权重利用权重线性组合(WLC)模型编制基于WLC的贵州省滑坡易发性区划图。
(2)根据历史滑坡灾点面积和滑坡致灾单因子子集面积计算滑坡发生确定性系数(CF),并据此确定滑坡主要的致灾因子。应用Logistic回归模型(LR)、GIS空间分析工具和统计软件SPSS寻求最合适的模型描述灾害是否发生(因变量)和致灾因子(自变量)之间的关系,并编制基于LR的滑坡易发性区划图。
(3)将自组织特征映射神经网络(SOM)应用于滑坡敏感性聚类分析中,对贵州省内历史滑坡分布在各二级致灾因子中的面积密度数据进行统一训练,反复调试,得出适合滑坡的步长、学习速率、训练次数、拓扑规则、输出结构等参数值,编制基于SOM的滑坡易发性区划图。
(4)将以上3种模型编制的滑坡易发性区划图进行统一比较,分析各模型的优劣性和适用范围。
(5)应用ArcGIS Server,将以上研究成果(包括各模型、滑坡致灾因子及图层、贵州省滑坡易发性区划图)进行网络发布,指导地方土地利用及基础设施建设规划。
Landslide is the most widespread geological hazard in the world. A lot of landslides were occurred in the recent years. Some of these landslides seriously threat personal and property safety because of their large scale. As a valuable tool for assessing current and potential landslide risks, landslide susceptibility mapping based on GIS was used for land-use planning and infrastructures layout, and developing the early-warning systems. With combination of landslides inventory and 10 causal factors such as elevation, slope, lithology and so on based on GIS, the paper established the landslide susceptibility map in study area of Guizhou province through using several mathematical models. The results of the study were used by relative departments to decrease the loss of lives and properties of the local people.
As intensive theoretical models, weighted linear combination model (WLC), logistic regression (LR) and self-organizing map neural network (SOM) have been introduced to evaluate landslides susceptibility in this paper. The achievements of the research work are as followings:
(1) Entropy-based weighting method was used to calculate objective weights of causal factors according to the area density of occurred landslides. Trapezoidal fuzzy number weighting (TFNW) approach was used to assess the importance of each subclasses of a causal factor. Finally, the landslides susceptibility map of Guizhou province in China was created by using WLC model.
(2) Selected the key factors of landslide according to Certainty Factor (CF) which was deduced from the area of landslides inventory and the area of subclasses of factors. With the helping of GIS spatial analysis tools and SPSS, LR model, which described the relationship of hazards (dependent) and the key factors, was used to create landslide susceptibility map.
(3) In order to get reasonable parameters of SOM, vectors which were composited by landslide area densities had been trained 100 times. Then, the landslide susceptibility map was established with SOM model.
(4) Three models and results of above were compared under the same conditions to get the distinguishing features and applicable conditions of each model.
(5) In order to guide land utilization and plan infrastructure, the landslide susceptibility map was published on web based on ArcGIS Server.
本论文将主客观线性加权模型(WLC)、Logistics回归模型(LR)、自组织特征映射神经网络模型(SOM),引入滑坡灾害敏感性评估中,主要完成了以下工作：
(1)根据历史滑坡的面积密度,以信息熵权理论计算一级指标的客观权重,应用梯形模糊数加权(TFNW)方法计算二级指标的主观权重,综合主客观权重利用权重线性组合(WLC)模型编制基于WLC的贵州省滑坡易发性区划图。
(2)根据历史滑坡灾点面积和滑坡致灾单因子子集面积计算滑坡发生确定性系数(CF),并据此确定滑坡主要的致灾因子。应用Logistic回归模型(LR)、GIS空间分析工具和统计软件SPSS寻求最合适的模型描述灾害是否发生(因变量)和致灾因子(自变量)之间的关系,并编制基于LR的滑坡易发性区划图。
(3)将自组织特征映射神经网络(SOM)应用于滑坡敏感性聚类分析中,对贵州省内历史滑坡分布在各二级致灾因子中的面积密度数据进行统一训练,反复调试,得出适合滑坡的步长、学习速率、训练次数、拓扑规则、输出结构等参数值,编制基于SOM的滑坡易发性区划图。
(4)将以上3种模型编制的滑坡易发性区划图进行统一比较,分析各模型的优劣性和适用范围。
(5)应用ArcGIS Server,将以上研究成果(包括各模型、滑坡致灾因子及图层、贵州省滑坡易发性区划图)进行网络发布,指导地方土地利用及基础设施建设规划。
Landslide is the most widespread geological hazard in the world. A lot of landslides were occurred in the recent years. Some of these landslides seriously threat personal and property safety because of their large scale. As a valuable tool for assessing current and potential landslide risks, landslide susceptibility mapping based on GIS was used for land-use planning and infrastructures layout, and developing the early-warning systems. With combination of landslides inventory and 10 causal factors such as elevation, slope, lithology and so on based on GIS, the paper established the landslide susceptibility map in study area of Guizhou province through using several mathematical models. The results of the study were used by relative departments to decrease the loss of lives and properties of the local people.
As intensive theoretical models, weighted linear combination model (WLC), logistic regression (LR) and self-organizing map neural network (SOM) have been introduced to evaluate landslides susceptibility in this paper. The achievements of the research work are as followings:
(1) Entropy-based weighting method was used to calculate objective weights of causal factors according to the area density of occurred landslides. Trapezoidal fuzzy number weighting (TFNW) approach was used to assess the importance of each subclasses of a causal factor. Finally, the landslides susceptibility map of Guizhou province in China was created by using WLC model.
(2) Selected the key factors of landslide according to Certainty Factor (CF) which was deduced from the area of landslides inventory and the area of subclasses of factors. With the helping of GIS spatial analysis tools and SPSS, LR model, which described the relationship of hazards (dependent) and the key factors, was used to create landslide susceptibility map.
(3) In order to get reasonable parameters of SOM, vectors which were composited by landslide area densities had been trained 100 times. Then, the landslide susceptibility map was established with SOM model.
(4) Three models and results of above were compared under the same conditions to get the distinguishing features and applicable conditions of each model.
(5) In order to guide land utilization and plan infrastructure, the landslide susceptibility map was published on web based on ArcGIS Server.
引文
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