汶川震区暴雨泥石流危险范围预测研究

汶川地震发生后,灾区暴雨泥石流活动进入一个新的活跃期。根据对北川震区2008年9月24日暴雨泥石流调查,泥石流流域中地震诱发大量滑坡导致松散物源巨大,泥石流过程的洪峰流量比通常的要大数倍,应用以往泥石流危险范围预测模型进行计算的结果与实际的误差较大。因此,需要建立适用于强震区的泥石流危险范围预测方法。本文以9.24北川暴雨泥石流为典型实例,结合野外调查,利用震后高分辨航空图像和9.24暴雨后SPOT5图像分别提取泥石流发生前流域中滑坡物源储量及发生后形成的堆积扇特征数据,应用多元回归方法建立了汶川震区泥石流危险范围预测模型,该方法可用于估算泥石流最大堆积距离和堆积宽度。验证和应用结果表明:该模型适用于强震区泥石流危险范围的预测,模型方法可为震区重建中安全地段选择和未来地震区风险管理提供重要依据。     

基于BP神经网络的泥石流平均流速预测

泥石流平均流速是泥石流防治工程中不可缺少的重要参数,准确地预测泥石流平均流速对于泥石流防治工程的设计是至关重要的。将BP神经网络应用于泥石流平均流速的预测:将泥石流平均流速的影响因素--泥沙平均粒径、泥深、沟床比降和泥石流密度作为BP神经网络的输入单元,通过对云南东川蒋家沟泥石流观测数据的训练与预测建立了泥石流平均流速的BP神经网络预测模型。将预测结果与东川公式和曼宁修正公式的计算结果进行对比:曼宁修正公式和东川公式预测结果最大误差分别为27%和7.3%,BP神经网络的预测结果最大误差仅为3.2%,BP神经网络的预测精度是最高的,可见此方法对泥石流平均流速预测具有适用性和准确性。最后应用此方法预测了乌东德水电站近坝库区内的3条泥石流的平均流速分别为12.8 m/s、11.3 m/s和13.0 m/s,为库区泥石流防治工程提供了可靠的参考数据。     

泥石流堆积物粒度分布特征影响因素分析及分形维数预测

泥石流堆积物作为泥石流发育最终的产物,含有大量与泥石流发生过程和发育特征相关的信息,能够反映泥石流灾害程度和活动强度。研究表明,泥石流堆积物颗粒具有明显的自相似性和无标度区间,运用分形理论,计算泥石流堆积物颗粒分布的分维数。分析分维数与主沟长度、泥砂补给段长度比、主沟平均比降、流域最大相对高差和松散物源量的关系,结果表明分维数与各因素之间存在较强的非线性响应关系。以乌东德库区泥石流实测数据为例,以上述的5个因素作为输入单元,建立了泥石流堆积物分维数支持向量机预测模型,并对分维数进行了预测,其预测结果的最大误差为1.25%,说明预测值与实测值吻合度较高。综合表明支持向量机预测模型能够较好地模拟和泛化数据,是一种行之有效的泥石流堆积物分形维数预测方法,可用于不具备筛析条件的泥石流堆积物粒度分布特征的预测与研究,进而可为研究泥石流的形成机理、类型、危险度和堆积物的形成演化特征及物理力学性质提供一个新思路。     

泥石流堆积过程数值模拟及防灾效益评估方法

根据动量守恒和质量守恒原理 ,研究建立了泥石流堆积过程的数学模型 ,运用有限差分法求解数学模型 ,用以模拟泥石流堆积的动态过程。在此基础上 ,结合云南省东川市深沟泥石流堆积区的实际情况 ,对泥石流灾害的危险范围和程度进行了分析评价 ;结合各类受灾体经济损失评价 ,对比防灾工程造价 ,进行了减灾效益分析评价。     

磐石市富太镇泥石流危险性评价与危险范围预测

磐石市富太镇地区泥石流灾害较为发育,给当地人民的生命财产、交通安全构成了一定的威胁。为了为合理制定泥石流防治方案和进行泥石流防治工程设计提供依据,文章采用模糊数学综合评判法和泥石流最大危险范围预测模型分别对富太地区的21条泥石流进行了危险性评价和最大危险范围的预测。评价和预测结果表明:本区泥石流危险程度基本都属于轻微级,而且它们的最大危险范围也较小,符合富太地区的实际情况。     

基于φ值粒度成分分析的泥石流动力特性

为了更准确地预测乌东德水电站库区方山果沟泥石流的动力特性,首先采用概率累积曲线法对沟口堆积颗粒进行沉积环境的定性判断,然后采用图算粒度参数法对泥石流堆积物进行分析,并对泥石流的动力特性进行初步的定性判断,最后根据堆积颗粒受力分析结合初步的动力特性的定性判断结果建立固体颗粒的力平衡方程。通过对方山果沟泥石流堆积物进行分析,得出方山果泥石流沟具有以下动力特征:方山果沟泥石流堆积物以碎石为主,分选很差;在搬运能力极强的重力斜坡作用下以推移、滚动搬运的方式为主。在此基础上计算得到方山果沟泥石流流速ub=8.67m/s。     

泥石流堆积形态影响要素的数值模拟

本文运用泥石流堆积数值模拟方法,对影响泥石流堆积形态的主要因素分别进行了变化趋势的模拟分析,展示了堆积区地形坡度的差异、泥石流体重度变化和一次泥石流冲出量大小不同等所产生的泥石流堆积形态和空间展布范围的变化规律.研究结果可应用于分析预测不同条件下、不同类型的泥石流灾害发生范围及强度分布,进而为灾害风险评估提供基础.     

泥石流堆积运动特征分析

以云南省东川市深沟泥石流堆积区为研究对象, 根据深沟可能爆发泥石流灾害的区域范围、规模、性质和介质特征, 按爆发20年一遇(频率5%)和100年一遇(频率1%)的泥石流灾害预测规模, 运用数值模拟方法模拟了泥石流爆发历时过程的堆积运动特征及空间分布形态; 分析了不同规模泥石流堆积运动过程中, 泥石流堆积厚度及堆积运动速度在空间上和时间上的变化发展趋势.      

基于FLO-2D的都江堰市龙池镇黄央沟泥石流数值模拟

利用HEC-HMS软件结合ArcGIS技术模拟都江堰市龙池镇黄央沟2010年8月13日泥石流的清水流量过程线,然后生成的清水过程线输入到FLO-2D软件中模拟了黄央沟泥石流的运动和堆积过程,计算了堆积的深度和分布范围。文中分析比较了模拟结果与实际调查结果,误差较小。可见基于FLO-2D的泥石流规模预测具有一定的应用意义,预测结果可以为泥石流的防治和预警提供依据。     

山区公路地质灾害减灾决策支持系统——以西藏古乡沟泥石流为例

由于复杂地形条件和地质条件以及降雨、地下水等因素的影响,山区公路易受到泥石流、滑坡、崩塌、溜砂坡等地质灾害的危害。文章在对山区公路地质灾害模型研究和分析的基础上,建立了滑坡稳定性分析、泥石流活动性分析、泥石流危险范围预测与危险性分区、滑坡区公路整治方案优选、拦砂坝优化设计等灾害分析模型和减灾决策模型,通过数据库、分析模型和决策模型的集成,建立了基于组件式GIS的山区公路地质灾害减灾决策支持系统。作为系统应用的实例,文章最后讨论了系统在古乡沟泥石流危险范围预测的应用,预测了一定条件下泥石流堆积扇上的泥深,泥石流运动过程中出现的最大泥深、最大速率、最大动量和最大动能,为古乡沟泥石流的预防和治理提供了科学依据。     

Regional debris flow susceptibility analysis based on principal component analysis and self-organizing map: a case study in Southwest China

A method was developed to analyze the susceptibilities of 541 regional basins affected by debris flows at the Wudongde Dam site in southwest China. Determining susceptibility requires information on source material quantity and occurrence frequency. However, the large number of debris flows can hinder the individual field investigation in a each small basin. Factors that may trigger debris flows can be identified using remotely sensed interpretation information. Susceptibility analysis can then be conducted based on these factors. In this study, SPOT5 satellite imagery, digital elevation models (DEM), a lithology distribution map, and rainfall monitoring data were used to identify 12 debris flow trigger factors: basin relief ratio, slope gradient in the initiation zone, drainage density, downslope curvature of the main channel, vegetation coverage, main channel aspect, topographic wetness index, Melton’s ruggedness number, lithology, annual rainfall, form factor, and cross-slope curvature of the transportation zone. Principal component analysis was used to obtain the eight principal components of these factors that contribute to susceptibility results. Then, a self-organizing map method was adopted to analyze the principal components, which resulted in a debris flow susceptibility classification. Field validation of 26 debris flow basins was used to evaluate the errors of the susceptibility classification, as well as assess the causes of such errors. The study found that principle component analysis and self-organizing map methodologies are good predictors of basin susceptibility to debris flows.     

泥石流的二维数学模型

泥石流是在重力作用下,由砂粒石块和水等组成的固液混合物,是一种发生于山区的复杂的地质灾害现象。泥石流主要是由暴雨诱发引起的,它沿着复杂的三维地形高速流动,具有流体流动的特性。为了模拟泥石流的运动规律,预测降雨诱发的泥石流的到达距离和泛滥范围,减少和避免泥石流引起的灾害,把泥石和雨水组成的固液混合物假定为遵循均匀、连续、不可压缩的、非定常的牛顿流体运动规律。基于质量守恒方程和Naiver-stokes方程,采用深度积分方法,推导出了一个模拟泥石流运动的二维数学模型。所有方程式可用有限差分法来求解。结合GIS,该模型可用于预测泥石流的流动距离和泛滥范围,以及泛滥范围内的危险房屋和路段,也可以用于泥石流灾害的风险性分析。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.     

基于突变模型的泥石流危险度评价

基于突变理论模拟了乌东德地区泥石流的爆发。突变模型适于表达开放系统,假设危险度的影响因子是突变模型中的控制变量,利用初等突变模型对泥石流危险度进行多目标评估,根据当地的实际情况制定对应的评价标准。结果表明,模型在奇点处发生突变时泥石流爆发,该结果与传统的灰关联法计算结果基本一致,且与实际调查的情况较为吻合。     

基于联系期望的库岸泥石流危险性评价以乌东德库区为例

库岸泥石流危险性评价, 受具有随机性、非线性与未确知性特点的诸多因素的影响与控制, 是一个极其复杂的难题。以金沙江下游乌东德库区为例, 据其特有的地质环境条件, 选取爆发历史、地质条件、地形条件、诱发因素共4类10种泥石流影响因子, 并建立分级标准将库岸泥石流危险性分为4个等级:极度危险、高度危险、中度危险、轻度危险。应用集对分析理论分析利用区间数表示的泥石流影响因子, 建立了基于联系期望概念的库岸泥石流危险性评价新模型, 可统一分析泥石流危险性评价指标的区间形式及演化态势。实例分析结果表明, 该方法评判结果可靠, 且能简化区间数关系的分析过程。     

Potential hazard analysis and risk assessment of debris flow by fuzzy modeling

Taiwan is a mountainous country, so there is an ever present danger of landslide disasters during the rainy seasons or typhoons. This study aims to develop a fuzzy-rule-based risk assessment model for debris flows and to verify the accuracy of risk assessment so as to help related organizations reduce losses caused by debris flows. The database is comprised of information from actual cases of debris flows that occurred in the Hualien area of Taiwan from 2007 to 2008. The established models can assess the likelihood of the occurrence of debris flows using computed indicators, verify modeling errors, and make comparisons between the existing models for practical applications. In the establishment of a fuzzy-based debris flow risk assessment model, possible for accounting it on the basis of far less information regarding a real system and the information can be of an uncertain, fuzzy or inexact character, the influential factors affecting debris flows include the average terrain slope, catchment area, effective catchment area, accumulated rainfall, rainfall intensity, and geological conditions. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment, with a resultant ratio of success 96?% and a normalized relative error 4.63?%.     

Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States

It has been recognized that wildfire, followed by large precipitation events, triggers both flooding and debris flows in mountainous regions. The ability to predict and mitigate these hazards is crucial in protecting public safety and infrastructure. A need for advanced modeling techniques was highlighted by re-evaluating existing prediction models from the literature. Data from 15 individual burn basins in the intermountain western United States, which contained 388 instances and 26 variables, were obtained from the United States Geological Survey (USGS). After randomly selecting a subset of the data to serve as a validation set, advanced predictive modeling techniques, using machine learning, were implemented using the remaining training data. Tenfold cross-validation was applied to the training data to ensure nearly unbiased error estimation and also to avoid model over-fitting. Linear, nonlinear, and rule-based predictive models including naïve Bayes, mixture discriminant analysis, classification trees, and logistic regression models were developed and tested on the validation dataset. Results for the new non-linear approaches were nearly twice as successful as those for the linear models, previously published in debris flow prediction literature. The new prediction models advance the current state-of-the-art of debris flow prediction and improve the ability to accurately predict debris flow events in wildfire-prone intermountain western United States.     

泥石流平均流速预测模型及敏感因子研究

为了探求泥石流平均流速敏感因子及影响因素耦合关系,本文采用BP神经网络和支持向量机模型对蒋家沟泥石流数据进行预测,对两种泥石流平均流速预测模型的学习与泛化能力进行比较,并对平均流速各影响因素的敏感程度进行分析,建立了泥石流平均流速敏感因子预测模型。结果表明:支持向量机的泛化能力优于BP网络,更适合样本数量较少的泥石流动态预测。沟道比降和不稳定层厚度是泥石流平均流速的主要影响因子,各因子之间存在复杂的耦合关系。基于不稳定层厚度和泥面比降的泥石流平均流速预测模型精度较高,能够定量描述泥石流动态与影响因子间的响应关系。研究成果可为泥石流防治提供依据。