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水力发电学报 ›› 2025, Vol. 44 ›› Issue (2): 1-14.doi: 10.11660/slfdxb.20250201

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编辑部推荐论文:暴雨型泥石流特征参数反演方法及透水格栅效能评价研究

  

  • 出版日期:2025-02-25 发布日期:2025-02-25

Inversion method for characteristic parameters of rainfall-induced debris flows and efficiency evaluation of drainage screen

  • Online:2025-02-25 Published:2025-02-25

摘要: 我国东南沿海地区丘陵山地广布,在台风暴雨等因素激发下,各类地质灾害频发。该地区由暴雨引发的泥石流灾害具有突发性、群发性和破坏性特点,当此类灾害发生在临近河道沟谷时,可能冲击水工建筑物甚至造成堵江等严重危害。由于灾害过程的高度不确定性,有效的动力学参数反演方法对分析灾害演化特征和制定有效的减灾措施具有重要意义。本研究以“5·8泰宁泥石流”事件为案例,提出了一种基于多输出支持向量回归机(M-SVR)子模型的参数反演方法。首先,在泥石流动力学计算模型Geoflow_SPH的基础上,构建并行调用框架,对包含内摩擦角、容重、平均物源厚度的触发特征参数组合进行数值模拟,生成了包含输入参数和运动特征的初始训练样本。随后,将该样本集(1000组)按比例划分训练集和测试集,并结合网格搜索技术训练得到M-SVR子模型。再后,使用该子模型对所建反演计算细分样本集(8000组)进行预测计算,以地勘报告中记录的3处控制断面泥石流流通速度为基准,通过计算预测结果的均方误差(MSE),筛选出MSE值最小的参数组合作为最终的反演结果。最后,进一步分析透水格栅结构在阻滞泥石流运动和控制影响范围的作用。研究成果有助于明晰暴雨型泥石流的灾害演化规律,为减灾措施的科学应用提供理论支撑。

关键词: 泥石流, 多输出支持向量回归, 参数反演, 透水格栅

Abstract: Hilly and mountainous regions are extensively distributed along the southeast coast in China, where various geological disasters occur frequently under the influence of factors such as typhoons and heavy rains. Debris flow disasters triggered by heavy rains in this area are characterized by suddenness, cluster occurrence, and certain destructiveness, and they may impose an impact on hydraulic structures and even cause serious hazards such as flow blockage if occurring near river channels or valleys. Because of the high uncertainty in their process, effective dynamic parameter inversion methods are of great significance for analyzing the characteristics of their evolution and formulating effective disaster mitigation measures. This paper develops a new parameter inversion method based on the multi-output support vector regression (M-SVR) sub-model, taking the "5·8 Taining Debris Flow" event as a study case. First, based on the debris flow dynamics calculation model Geoflow_SPH, we construct a parallel calling framework to numerically simulate the triggering feature parameter combinations-including internal friction angle, unit weight, and average source thickness-and generate initial training samples that contain input parameters and motion characteristics. And the sample set (1000 groups) is divided into training sets and test sets in proportion, and the M-SVR sub-model is trained using grid search technology. Then, we use the sub-model to predict and calculate the subdivided inversion calculation sample set (8000 groups), taking as the benchmark the debris flow velocity recorded in the geological survey report at three control sections, and selecting the parameter combination with the minimum mean square error (MSE) as the final inversion result through calculating the predictions. Finally, we examine the role of the drainage screen structure in obstructing the movement of debris flow and controlling the range of impact. The research results help clarify the evolution of rainstorm-type debris flow disasters and lay a theoretical basis for application of various disaster mitigation measures.

Key words: debris flow, multi-output support vector regression, parameter inversion, drainage screen

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