泥石流灾害风险预测与回归分析：以西安喂子坪村泥石流灾害为对象

Risk prediction and regression analysis of debris flow disasters: Taking the debris flow disaster in Weiziping Village, Xi’an as the object

摘要

【目的】研究泥石流运动特性及关键影响参数，构建泥石流灾害风险预测模型，为评估泥石流对交通基础设施灾害风险提供一定数据支撑。【方法】以陕西省西安市喂子坪村泥石流灾害为研究对象，建立基于深度积分连续介质力学理论的泥石流运动模型，选取摩擦系数和泥石流流量作为关键参数，探究泥石流流速和堆积区域泥深变化规律，并评价泥石流出沟处区域风险等级。结合多元线性回归、多项式回归和支持向量机模型，建立泥石流流量、摩擦系数与流速、泥深之间的精准预测方程。【结果】泥石流流速与流量之间存在显著的正相关，流速在不同流量条件下波动，最高流速范围为2.58~8 m·s^-1;泥深随流量的增加而不断增大，最大泥深范围为0.5~4 m;摩擦系数与泥石流的流速和泥深均呈现负相关，最高流速和最大泥深随着摩擦系数的增大整体呈下降趋势。风险预测结果表明，泥石流风险性随流量增大显著升高，摩擦系数的增大可有效降低风险，在较低泥石流流量时效果显著，而较大泥石流流量下摩擦阻力作用效果不显著。支持向量机模型对泥石流最高流速的预测效果较好，而多项式回归模型对泥石流堆积区域最大泥深的预测效果最佳。【结论】对泥石流流量进行监控以预判泥石流流速和堆积区域泥深，对于灾前预估泥石流灾害风险等级、灾后辅助评估泥石流灾害程度具有重要支撑作用。

Abstract

[Objective] To study the movement characteristics and key influencing parameters of debris flow, construct a debris flow disaster risk prediction model, and provide data support for assessing the disaster risk of transportation infrastructure caused by debris flows. [Methods] Taking the debris flow disaster in Wei Ziping Village, Xi'an City, Shanxi Province as the research object, a debris flow movement model based on the depth-integrated continuum mechanics theory was established. The friction coefficient and debris flow discharge were selected as key parameters to explore the variation laws of debris flow velocity and mud depth in the accumulation area, and evaluate the risk level of the area where the debris flow exits the gully. Combined with the multiple linear regression, polynomial regression and support vector machine models, the prediction equations for the relationship between debris flow discharge, friction coefficient and flow velocity, and mud depth were established. [Results] There is a significant positive correlation between the flow velocity and the flow rate of debris flow. The flow velocity fluctuates under different flow rate conditions, with the maximum flow velocity ranging from 2.58 m·s^-1 to 8 m·s^-1. The mud depth keeps increasing with the increase of flow rate, and the maximum mud depth range is 0.5 m to 4 m. The friction coefficient is negatively correlated with both the flow velocity and the mud depth of the debris flow. With the increase of the friction coefficient, the maximum flow velocity and the maximum mud depth show an overall downward trend. The risk prediction result show that the risk of debris flow increases significantly with the increase of debris flow. The increase of the friction coefficient can effectively reduce the risk, and the effect is significant at a lower debris flow, while the effect of frictional resistance is not significant at a larger debris flow. The support vector machine model has a better prediction effect on the maximum flow velocity of debris flow, while the polynomial regression model has the best prediction effect on the maximum mud depth in the debris flow accumulation area. [Conclusion] Monitoring the flow of debris flows to predict the flow velocity and the depth of the accumulated sediment in the affected area plays a crucial supporting role in pre-disaster assessment of the risk level of debris flow and post-disaster auxiliary evaluation of the severity of debris flow.

关键词

泥石流 / 运动参数 / 动力特性 / 风险评估 / 风险预测模型 / 影响因素 / 支持向量机模型 / 工程地质灾害

Key words

debris flow / motion parameters / dynamic characteristics / risk assessment / risk prediction model / influencing factors / support vector machine model / engineering geological disasters

基金资助

国家重点研发计划课题(2021YFB2600605)

国家重点研发计划课题(2021YFB2600600)

河北省交通厅科技计划项目(JD-202007)