【作者】 赵岩；

【导师】 孟兴民；

【作者基本信息】 兰州大学 ， 地理学·自然地理学， 2020， 博士

【摘要】 泥石流是山区的主要地质灾害之一。对于不同发生频率的泥石流沟,中高频泥石流沟由于受重视程度较高,防灾措施相对完善,但潜在的低频泥石流沟容易被忽视,特别是随着山区人口增加,这些沟沟口较平坦的地方成为居民的理想居住环境,然而一旦遭遇罕见暴雨激发泥石流,常造成严重的灾害。这类泥石流沟因其隐蔽性而常被忽视,相关研究薄弱,特别是在山区勘察困难的条件下,如何快速有效的对其识别显得尤为迫切。在此背景下,本文通过资料收集、野外调查、数据建库和模型构建等方法,基于机器学习技术,对白龙江流域潜在低频泥石流沟进行识别和预测,主要工作和结论如下:(1)基于泥石流形成条件分析,认为地貌条件是泥石流形成的“相对稳定的主控因子”,物质条件为“相对动态的控制因子”,激发条件为“相对随机的激发因子”。并基于地貌参数对泥石流沟发育阶段进行了定量划分。(2)基于空间深度学习模型构建了泥石流堆积扇遥感影像自动识别模型,在训练区模型的召回率为93.8%,在检测区模型的召回率为90.9%,并新发现泥石流沟20个。表明泥石流堆积扇识别模型总体识别效果较好,尤其是对坡面泥石流的识别,是现有方法的重要补充。(3)基于机器学习回归模型构建了泥石流发生频率的预测模型,并发现对泥石流发生频率影响最大的因子是10分钟平均降水量,次之的是植被覆盖指数。总体上,在研究区对泥石流发生频率影响最大的是激发条件,物质条件次之。模型预测的研究区泥石流发生频率分布图可为泥石流减灾做科学指导。(4)基于机器学习分类模型构建了低频泥石流沟识别模型,可对研究区低频泥石流沟进行快速识别。研究发现综合物质条件是低频泥石流沟的主要影响因子,低频泥石流沟主要发育在岩性较弱,滑坡较少,以及植被覆盖较高的地方。模型预测的研究区低频泥石流沟分布可为泥石流隐患点勘察和预防提供支持。综上,通过建立的潜在低频泥石流沟识别方法和模型,深入了解了低频泥石流沟的发育条件,是对潜在低频泥石流灾害预防的有效对策。可以对研究区内潜在的低频泥石流沟进行快速有效的识别,弥补现有方法的不足,可为泥石流隐患点勘察和预防、危险性评价以及国土空间规划等提供新的技术和理论支撑。更多还原

【Abstract】 Debris flow is one of the major geohazards in mountainous areas.For debris flows with different frequencies,medium-high frequency debris-flow catchments are highly valued and prevention measures for them are relatively complete.However,potential low-frequency debris-flow catchments are easily be ignored.Especially with the increasing of the population in mountainous areas,flat places at the outlets of some debris flow catchments become the ideal living places.Once a rare heavy rain triggers the debris-flow,it will cause major disasters.This type of debris-flow catchment is often overlooked because of its concealment,and related research is weak.Especially under the strenuous conditions of mountain exploration,how to identify them quickly and effectively is extremely urgent.In this paper,we identified and predicted the potential low-frequency debris-flow catchments in the Bailong River basin based on Machine Learning technology through methods such as data collection,field surveys,data database construction,and model building.The main work and conclusions are as follows:(1)Based on the analysis of the debris-flow formation conditions,this paper considers that the geomorphological conditions are the "relatively stable main controlling factors",the material conditions are "relatively dynamic controlling factors",and the triggering conditions are "relatively random excitation factors".Based on the geomorphic parameters,the development stages of debris flow catchment are quantitatively divided.(2)A remote sensing automatic identification model for debris-flow fans was constructed based on spatial deep-learning model.The recognition results were that the model had a recall rate of 93.8% in the training area,in the detection area,the recall rate was 90.9%,and 20 new debris flows were found.It shows that the debris-flow fan identification model has a good overall recognition effect,especially the identification of slope debris-flow,which fills the deficiency of the existing methods.(3)A debris flow frequency prediction model was built based on the regression model,and a good simulation prediction effect was obtained.It was shown that the factor that has the greatest influence on the frequency of debris flow is the 10-minute average precipitation,followed by the vegetation cover index.In general,the most important influence on the frequency of debris flow in the study area is the excitation condition,followed by the material condition.The frequency distribution map of debris-flow in the study area predicted by the model can provide scientific guidance for debris-flow disaster reduction.(4)A low-frequency debris flow identification model was constructed based on the classifier model,which can quickly identify low-frequency debris flows in the study area.It was found that the comprehensive material conditions are the main influencing factors of the low-frequency debris flows,which are mainly developed in places with weak lithology,few landslides and high vegetation coverage.Low-frequency debris flow distribution in the study area predicted by the model can provide technical support for the investigation and prevention of hidden danger points of debris flow.In summary,the identification method and model of potential low-frequency debris-flow catchments established in this paper are of great significance for understanding the characteristics of the development conditions of low-frequency debris flows,and are effective countermeasures for the prevention of potential lowfrequency debris flows.It can quickly and effectively identify the potential lowfrequency debris flow catchments hidden in the study area,make up for the shortcomings of traditional methods,and provide new technical and theoretical support for the investigation and prevention of hidden danger points of debris flow.更多还原