GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

基于OOD泛化性验证和深度全连接神经网络的泥石流易发性评价方法

提升易发性评价精度有助于山区泥石流灾害早期的识别和监测预警。大部分机器学习模型在训练、测试集合上表现良好,但实际应用过程精度较差,不利于工程选址规划和防灾减灾,如何提高机器学习模型评价精度与泛化性具有重要意义。选取深度全连接神经网络,与梯度提升树、随机森林模型和贝叶斯网络等机器学习方法共同进行模型精确性评价和OOD(out-of-distribution)泛化性验证,从而找出在训练、预测和应用中均具有较高精度的方法。以四川省雅安市为例,采用小流域单元进行区域网格划分,将数据集合按7∶3比例随机分为训练集和测试集,使用经验法则(3-sigma)剔除异常数据,并基于多变量(Iterative Imputer)和K-近邻法对缺失值填充进行泥石流灾害易发性评价。在泥石流易发性因子的共线性、敏感性和预测能力的分析结果基础上,选定14个易发性因子构建模型评价指标体系,进行泥石流易发性评价与对比。通过对模型的精确性评价及OOD泛化性验证发现:深度全连接神经网络模型曲线下的面积(AUC)、准确率(Acc)、召回率(Recall)的值比梯度提升树等的计算结果分别超出了0.027、0.02、0.02,而平均绝对值误差(MAE)降低了0.003;OOD泛化性验证准确度超出了0.056。研究表明,深度全连接神经网络对于泥石流易发性评价的预测效果较好,能够提高泥石流评价的精度,增加评价的适应性,可为泥石流易发性评价提供新思路。

基于物理模型试验的堰塞坝冲刷溃决过程研究

中国是堰塞湖灾害最严重的国家之一,堰塞湖对上游淹没区和下游溃决演进区的人民生命财产安全构成巨大威胁,深入掌握堰塞坝冲刷溃决过程可为应急泄流道的设计和下游应急避险措施的制定等提供重要科技依据。以2018年金沙江白格滑坡堰塞湖为研究背景,采用室内物理模型试验的手段对堰塞坝冲刷溃决过程进行了系统研究。结果表明:堰塞坝冲刷溃决过程一般可分为4个阶段:过流孕育阶段、溯源侵蚀阶段、溃决发展阶段及河床再平衡阶段,其中:当溯源冲刷的陡坎追溯到上游坡顶,泄流槽进口断面在侵蚀作用下突然拓宽,泄流槽将连通形成底坡i>0的斜坡道,进而导致水流流速和流量突然增大,堰塞坝进入溃决快速发展的阶段。试验进一步探究了泄流槽开挖位置、开挖深度和宽度对溃决过程的影响。研究发现:当泄流槽开挖宽度不变,深度增大时,洪峰流量降低、峰现时间延迟,溃决流量过程线更为平坦;当泄流槽开挖深度不变,宽度增大时,峰现时间延迟。最后,对泄流槽的优化设计提出建议,泄流槽位置宜布置在坝顶高程最低的垭口,以减小洪峰流量,缩短溃决历时;开挖泄流槽时,应优先考虑加大泄流槽深度,最大限度地降低溃决时的堰塞湖水位。