滑坡、泥石流地质灾害评价方法研究
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摘要
滑坡和泥石流灾害是常见的地质次生灾害，对人们的生产生活产生巨大的威胁并对区域经济的稳定、健康发展具有巨大的破坏作用。由于滑坡、泥石流地质灾害本身是一个非线性的复杂系统，人们到目前为止对其发生的机理和影响因素还没有清楚的认识,在滑坡和泥石流地质灾害评价过程中还存在很大的不确定性。进行滑坡和泥石流地质灾害评价方法研究,不仅具有科学理论上的价值,更具有很大的应用价值。通过此项研究可以提高这两类地质灾害评价的准度和精度，对制定区域经济中长期规划具有指导作用,对人们的生命财产安全和社会经济的发展具有重要的意义。
国内外的科研工作者在滑坡和泥石流地质灾害评价方法方面已经进行的大量的研究工作，提出的评价方法大致可分为：定性的评价方法、确定性的方法、统计分析方法和基于人工智能的方法,以及基于“3S”技术的评价方法。本文提出了基于FAN (Forest Augmented Naive Bayesian,即森林增强型贝叶斯网络模型)模型的滑坡地质灾害脆弱性评价方法和基于贝叶斯网模型的泥石流地质灾害危险性评价方法,结合地理信息系统(GIS)技术,研究并开发了基于GIS的滑坡地质灾害脆弱性评价系统和泥石流地质灾害危险性评价系统。完成的研究工作及取得的主要进展如下
(1)在分析现有评价模型存在的问题的基础上,根据四川攀西地区尺度范围,针对滑坡地质灾害脆弱性评价过程中存在的问题，选择了FAN作为滑坡地质灾害脆弱性评价的模型；考虑到中国大陆地区尺度范围比较大,针对现有泥石流地质灾害危险性评价模型存在的问题,选择了贝叶斯网络模型作为泥石流地质灾害危险性评价的模型。
(2)滑坡地质灾害脆弱性是指一个地形单元发生滑坡地质灾害的概率大小,不考虑灾害发生的频率和规模。滑坡地质灾害的发生受很多复杂因素的影响，其评价具有很大的不确定性,因此,滑坡地质灾害脆弱性评价是一件很有挑战性的工作。根据滑坡地质灾害发生的机理和影响因素,本研究建立了滑坡地质灾害脆弱性评价指标体系,并提出了基于FAN模型和专家先验知识的滑坡地质灾害脆弱性评价方法。在两个干扰性不同的样本集上,分别对FAN模型和人工神经网络模型进行交叉验证。验证结果表明FAN模型在干扰性较小样本集2上(命中率：84.13%；精度：78.51%)具有很好的命中率和精度；与人工神经网络模型的性能对比结果表明FAN模型的评价性能较好,鲁棒性更好。最后分别用FAN模型和人工神经网络模型生产出了攀西地区的滑坡地质灾害脆弱性空间分布等级图。两个等级图对比分析的结果表明，FAN模型生产出的等级图的空间分布更合理。
(3)泥石流地质灾害是一个非线性的复杂系统,灾害的发生受很多因素的影响,其评价过程具有很大的不确定性,是一个尚未得到很好解决的科学难题。根据泥石流地质灾害发生的机理和影响因素,本研究建立了泥石流地质灾害危险性评价指标体系,并提出了基于贝叶斯网络(BN)模型和专家先验知识的泥石流地质灾害危险性评价方法。在两个干扰性不同的样本集上，分别对贝叶斯网络模型、人工神经网络模型和支持向量机模型进行了交叉验证。在干扰性较小的样本集2上贝叶斯网络模型(命中率85.66%；误测率：8.23%；精度：89.63%)和人工神经网络模型(命中率：81.63%误测率：3.48%；精度：91.27%)的性能都很高,比支持向量机(命中率：73.44%误测率：8.17%；精度：85.31%)的性能好。在干扰性较大的样本集1上,人工神经网络(命中率：34.60%；精度：85.41%)和支持向量机(命中率：22.32%；精度：84.55%)的评价性能明显下降,而贝叶斯网络模型(命中率：76.99%；精度：76.53%)仍保持着较好的评价性能,这就说明贝叶斯网络模型具有较好的抗干扰性,鲁棒性更好。最后分别用贝叶斯网络模型、人工神经网络和支持向量机模型分别生产出了中国大陆地区的泥石流地质灾害危险性空间分布等级图。三幅等级图对比分析的结果表明：贝叶斯网络模型生产出的泥石流地质灾害危险性空间分布等级图更符合中国大陆的实际情况和调查数据的空间分布规律。交叉验证和评价结果对比分析的结果表明，贝叶斯网络模型能很好的处理泥石流地质灾害评价中的不确定性问题，是一个适合于大尺度范围(国家尺度)的泥石流地质灾害危险性评价模型。
(4)滑坡和泥石流地质灾害评价原型系统开发是实现评价方法业务化运行的关键。本研究从充分利用MATLAB实现的贝叶斯网络模型软件包的开源性考虑,来降低模型本身算法的开发成本。空间数据的处理以及评价结果数据的保存和展示都需要用到地理信息系统功能,因此与GIS系统的集成也是系统开发的关键技术。在研究ArcGIS Engine、C#和MATLAB Engine集成技术的基础上,结合滑坡、泥石流地质灾害评价模型,本研究设计并开发出了基于GIS的滑坡地质灾害脆弱性评价系统和泥石流地质灾害危险性评价系统。评价系统为滑坡、泥石流地质灾害评价、管理、减灾和防灾工作提供了工具和决策的支持；系统设计和集成的技术思路为原型系统的系统集成和快速开发提出了新的思路。
通过本文的研究,无论是理论上的科学性,还是滑坡、泥石流地质灾害评价结果的可靠性,均表明贝叶斯网络模型可以很好的处理滑坡、泥石流地质灾害评价过程中的不确定性问题,在地质灾害评价方面具有巨大的优越性，同时也显示出其广阔的应用前景。
Landslide and debris flow are common geological hazard. They can induce a series of disasters that may pose a serious threat to lives, property, and even economic development. The comprehensive assessment of landslide and debris flow hazard is a challenging task due to the uncertainties and complexity of various related factors. In this study, based on in-depth study and comprehensive analysis of previous research results, we proposed FAN (Forest Augmented Naive Bayesian) model for landslide susceptibility assessment and BN (Bayesian network) model for debris flow hazard assessment, and developed a GIS-based landslide susceptibility and debris flow hazard assessment systems. The main research work and key developments are as follows:
(1) Analyzed the problems of the existing landslide and debris flow assessment model. According to the characteristics of the study area, FAN model was chosen as a model of the landslide hazard susceptibility assessment, and BN model was chosen as a model of the debris flow hazard assessment.
(2) Landslide susceptibility is a full of challenge task due to the uncertainties and complexity of multiple related factors. Landslide susceptibility refers to the possibility of landslide occurrence of a terrain unit. Susceptibility does not consider the temporal probability of failure (i.e., when or how frequently landslides occur), nor the magnitude of the expected landslide. Factors associated with landslide susceptibility assessment are selected and a novel methodology for landslide susceptibility assessment based on FAN model and domain knowledge was proposed. Cross-validations of FAN model and ANNs (Artificial Neural Networks) were conducted on two different sample datasets. The landslide susceptibility maps of Pan-Xi district are produced using FAN model and ANNs, respectively. The results indicated that FAN model has excellent anti-interference, good robustness, and proves to be an alternative method for landslide susceptibility assessment.
(3) The comprehensive assessment of debris flow hazard risk is a challenging task due to the uncertainties and complexity of various related factors. A reasonable and reliable assessment should be based on sufficient data and assessment approaches reflecting the actual situation. Factors associated with debris flow hazard assessment are selected and a novel methodology for assessing debris flow hazard based on a BN and domain knowledge is presented. Based on authoritative records of debris flow hazards and geomorphologic and environmental data for the Chinese mainland, approaches based on BN, SVM (support vector machine) and ANNs (artificial neural networks) were compared. The results show that BN provides a higher probability (85.66%) of hazard detection, a better precision (89.63%), and a larger AUC (area under the receiver operating characteristic curve) value (0.95) than SVM and ANNs. The BN-based model is useful for mapping and assessing debris flow hazard risk on a national scale.
(4) Based on ArcGIS Engine, C#and MATLAB Engine, we developed a GIS-based landslide susceptibility and debris flow hazard assessment system. The assessment system can provide decision-making support and tools for decision makers.
All the analysis of both the scientific theory and practical application show that BN model can deal with the uncertainty of landslide susceptibility and debris flow hazard assessment, has great advantages in the hazard assessment and broad application prospects.
国内外的科研工作者在滑坡和泥石流地质灾害评价方法方面已经进行的大量的研究工作，提出的评价方法大致可分为：定性的评价方法、确定性的方法、统计分析方法和基于人工智能的方法,以及基于“3S”技术的评价方法。本文提出了基于FAN (Forest Augmented Naive Bayesian,即森林增强型贝叶斯网络模型)模型的滑坡地质灾害脆弱性评价方法和基于贝叶斯网模型的泥石流地质灾害危险性评价方法,结合地理信息系统(GIS)技术,研究并开发了基于GIS的滑坡地质灾害脆弱性评价系统和泥石流地质灾害危险性评价系统。完成的研究工作及取得的主要进展如下
(1)在分析现有评价模型存在的问题的基础上,根据四川攀西地区尺度范围,针对滑坡地质灾害脆弱性评价过程中存在的问题，选择了FAN作为滑坡地质灾害脆弱性评价的模型；考虑到中国大陆地区尺度范围比较大,针对现有泥石流地质灾害危险性评价模型存在的问题,选择了贝叶斯网络模型作为泥石流地质灾害危险性评价的模型。
(2)滑坡地质灾害脆弱性是指一个地形单元发生滑坡地质灾害的概率大小,不考虑灾害发生的频率和规模。滑坡地质灾害的发生受很多复杂因素的影响，其评价具有很大的不确定性,因此,滑坡地质灾害脆弱性评价是一件很有挑战性的工作。根据滑坡地质灾害发生的机理和影响因素,本研究建立了滑坡地质灾害脆弱性评价指标体系,并提出了基于FAN模型和专家先验知识的滑坡地质灾害脆弱性评价方法。在两个干扰性不同的样本集上,分别对FAN模型和人工神经网络模型进行交叉验证。验证结果表明FAN模型在干扰性较小样本集2上(命中率：84.13%；精度：78.51%)具有很好的命中率和精度；与人工神经网络模型的性能对比结果表明FAN模型的评价性能较好,鲁棒性更好。最后分别用FAN模型和人工神经网络模型生产出了攀西地区的滑坡地质灾害脆弱性空间分布等级图。两个等级图对比分析的结果表明，FAN模型生产出的等级图的空间分布更合理。
(3)泥石流地质灾害是一个非线性的复杂系统,灾害的发生受很多因素的影响,其评价过程具有很大的不确定性,是一个尚未得到很好解决的科学难题。根据泥石流地质灾害发生的机理和影响因素,本研究建立了泥石流地质灾害危险性评价指标体系,并提出了基于贝叶斯网络(BN)模型和专家先验知识的泥石流地质灾害危险性评价方法。在两个干扰性不同的样本集上，分别对贝叶斯网络模型、人工神经网络模型和支持向量机模型进行了交叉验证。在干扰性较小的样本集2上贝叶斯网络模型(命中率85.66%；误测率：8.23%；精度：89.63%)和人工神经网络模型(命中率：81.63%误测率：3.48%；精度：91.27%)的性能都很高,比支持向量机(命中率：73.44%误测率：8.17%；精度：85.31%)的性能好。在干扰性较大的样本集1上,人工神经网络(命中率：34.60%；精度：85.41%)和支持向量机(命中率：22.32%；精度：84.55%)的评价性能明显下降,而贝叶斯网络模型(命中率：76.99%；精度：76.53%)仍保持着较好的评价性能,这就说明贝叶斯网络模型具有较好的抗干扰性,鲁棒性更好。最后分别用贝叶斯网络模型、人工神经网络和支持向量机模型分别生产出了中国大陆地区的泥石流地质灾害危险性空间分布等级图。三幅等级图对比分析的结果表明：贝叶斯网络模型生产出的泥石流地质灾害危险性空间分布等级图更符合中国大陆的实际情况和调查数据的空间分布规律。交叉验证和评价结果对比分析的结果表明，贝叶斯网络模型能很好的处理泥石流地质灾害评价中的不确定性问题，是一个适合于大尺度范围(国家尺度)的泥石流地质灾害危险性评价模型。
(4)滑坡和泥石流地质灾害评价原型系统开发是实现评价方法业务化运行的关键。本研究从充分利用MATLAB实现的贝叶斯网络模型软件包的开源性考虑,来降低模型本身算法的开发成本。空间数据的处理以及评价结果数据的保存和展示都需要用到地理信息系统功能,因此与GIS系统的集成也是系统开发的关键技术。在研究ArcGIS Engine、C#和MATLAB Engine集成技术的基础上,结合滑坡、泥石流地质灾害评价模型,本研究设计并开发出了基于GIS的滑坡地质灾害脆弱性评价系统和泥石流地质灾害危险性评价系统。评价系统为滑坡、泥石流地质灾害评价、管理、减灾和防灾工作提供了工具和决策的支持；系统设计和集成的技术思路为原型系统的系统集成和快速开发提出了新的思路。
通过本文的研究,无论是理论上的科学性,还是滑坡、泥石流地质灾害评价结果的可靠性,均表明贝叶斯网络模型可以很好的处理滑坡、泥石流地质灾害评价过程中的不确定性问题,在地质灾害评价方面具有巨大的优越性，同时也显示出其广阔的应用前景。
Landslide and debris flow are common geological hazard. They can induce a series of disasters that may pose a serious threat to lives, property, and even economic development. The comprehensive assessment of landslide and debris flow hazard is a challenging task due to the uncertainties and complexity of various related factors. In this study, based on in-depth study and comprehensive analysis of previous research results, we proposed FAN (Forest Augmented Naive Bayesian) model for landslide susceptibility assessment and BN (Bayesian network) model for debris flow hazard assessment, and developed a GIS-based landslide susceptibility and debris flow hazard assessment systems. The main research work and key developments are as follows:
(1) Analyzed the problems of the existing landslide and debris flow assessment model. According to the characteristics of the study area, FAN model was chosen as a model of the landslide hazard susceptibility assessment, and BN model was chosen as a model of the debris flow hazard assessment.
(2) Landslide susceptibility is a full of challenge task due to the uncertainties and complexity of multiple related factors. Landslide susceptibility refers to the possibility of landslide occurrence of a terrain unit. Susceptibility does not consider the temporal probability of failure (i.e., when or how frequently landslides occur), nor the magnitude of the expected landslide. Factors associated with landslide susceptibility assessment are selected and a novel methodology for landslide susceptibility assessment based on FAN model and domain knowledge was proposed. Cross-validations of FAN model and ANNs (Artificial Neural Networks) were conducted on two different sample datasets. The landslide susceptibility maps of Pan-Xi district are produced using FAN model and ANNs, respectively. The results indicated that FAN model has excellent anti-interference, good robustness, and proves to be an alternative method for landslide susceptibility assessment.
(3) The comprehensive assessment of debris flow hazard risk is a challenging task due to the uncertainties and complexity of various related factors. A reasonable and reliable assessment should be based on sufficient data and assessment approaches reflecting the actual situation. Factors associated with debris flow hazard assessment are selected and a novel methodology for assessing debris flow hazard based on a BN and domain knowledge is presented. Based on authoritative records of debris flow hazards and geomorphologic and environmental data for the Chinese mainland, approaches based on BN, SVM (support vector machine) and ANNs (artificial neural networks) were compared. The results show that BN provides a higher probability (85.66%) of hazard detection, a better precision (89.63%), and a larger AUC (area under the receiver operating characteristic curve) value (0.95) than SVM and ANNs. The BN-based model is useful for mapping and assessing debris flow hazard risk on a national scale.
(4) Based on ArcGIS Engine, C#and MATLAB Engine, we developed a GIS-based landslide susceptibility and debris flow hazard assessment system. The assessment system can provide decision-making support and tools for decision makers.
All the analysis of both the scientific theory and practical application show that BN model can deal with the uncertainty of landslide susceptibility and debris flow hazard assessment, has great advantages in the hazard assessment and broad application prospects.
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