A Novel Prediction Model for Debris Flow Mean Velocity Based on Small Sample Data Taking Jiangjia Gully Watershed as an Example

IF 3.4 2区 工程技术 Q2 ENGINEERING, GEOLOGICAL
He Wei Kuang, Zhi Yong Ai, Gan Lin Gu
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引用次数: 0

Abstract

Among all the factors affecting the destructiveness of debris flow, the mean velocity is one of the most important characteristics. In this paper, we aim to apply a particle swarm optimization (PSO) based on the relevance vector machine (RVM) to predict the mean velocity. The PSO is used to optimize kernel parameters inside the RVM, whereas the RVM is responsible for completing the prediction task. Through sample training, a nonlinear relationship can be obtained, enabling a rapid prediction of the mean velocity for new samples. The debris flow dataset of Jiangjia Gully is used to evaluate the performance of PSO-RVM in this study. Besides, we further compare the prediction results of PSO-RVM with other prominent approaches, for example, the support vector machine (SVM), BP neural network (BP), and the RVM. The results show that the mean relative error (MRE) of PSO-RVM is only 0.69%. In addition, BP yields the highest MRE (27.61%), and the MRE (2.75%) corresponding to the RVM is lower than that (5.98%) yielded by the SVM. For the root mean square error (RMSE) and Theil's inequality coefficient (TIC), the PSO-RVM method still generates much lower RMSE (6.48%) and TIC (0.179%) values than the other three methods. Overall, compared with current debris flow prediction models, the PSO-RVM achieves high prediction accuracy, fewer optimization parameters, and low computational complexity. Finally, a sensitivity analysis is conducted to explore the dominative factors of debris flow.

以蒋家沟流域为例,基于小样本数据的泥石流平均流速新型预测模型
在影响泥石流破坏性的所有因素中,平均速度是最重要的特征之一。本文旨在应用基于相关性向量机(RVM)的粒子群优化(PSO)来预测平均速度。PSO 用于优化 RVM 内部的核参数,而 RVM 则负责完成预测任务。通过样本训练,可以获得非线性关系,从而快速预测新样本的平均速度。本研究使用蒋家沟泥石流数据集来评估 PSO-RVM 的性能。此外,我们还进一步比较了 PSO-RVM 与其他著名方法(如支持向量机(SVM)、BP 神经网络(BP)和 RVM)的预测结果。结果显示,PSO-RVM 的平均相对误差(MRE)仅为 0.69%。此外,BP 的平均相对误差(MRE)最高(27.61%),RVM 的平均相对误差(2.75%)低于 SVM 的平均相对误差(5.98%)。在均方根误差(RMSE)和 Theil 不等式系数(TIC)方面,PSO-RVM 方法产生的均方根误差(RMSE)(6.48%)和 TIC(0.179%)值仍远低于其他三种方法。总体而言,与目前的泥石流预测模型相比,PSO-RVM 预测精度高、优化参数少、计算复杂度低。最后,进行了敏感性分析,以探讨泥石流的主导因素。
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来源期刊
CiteScore
6.40
自引率
12.50%
发文量
160
审稿时长
9 months
期刊介绍: The journal welcomes manuscripts that substantially contribute to the understanding of the complex mechanical behaviour of geomaterials (soils, rocks, concrete, ice, snow, and powders), through innovative experimental techniques, and/or through the development of novel numerical or hybrid experimental/numerical modelling concepts in geomechanics. Topics of interest include instabilities and localization, interface and surface phenomena, fracture and failure, multi-physics and other time-dependent phenomena, micromechanics and multi-scale methods, and inverse analysis and stochastic methods. Papers related to energy and environmental issues are particularly welcome. The illustration of the proposed methods and techniques to engineering problems is encouraged. However, manuscripts dealing with applications of existing methods, or proposing incremental improvements to existing methods – in particular marginal extensions of existing analytical solutions or numerical methods – will not be considered for review.
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