Automatic identification of saltating tracks driven by strong wind in high-speed video using multiple statistical quantities of instant particle velocity

IF 3.1 3区 地球科学 Q2 GEOGRAPHY, PHYSICAL Aeolian Research Pub Date : 2024-09-21 DOI:10.1016/j.aeolia.2024.100940
Hongji Zhou , Fanmin Mei , Chuan Lin , Mengjie Pu , Aiguo Xi , Jinguang Chen , Jin Su , Zhibao Dong
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Abstract

The evolution of saltating tracks driven by strong wind remains unknown due to the low accuracy or recall rates of saltating particle tracking algorithms (SPTs). Manual identification of saltating tracks becomes a primary bottleneck because of low efficiency, restricting the development of new SPTs with high accuracy. Herein, we proposed an optimized tree model for automatically identifying saltating tracks in the high-speed video under strong wind through establishing the dataset with multiple statistical quantities of instant saltating velocity (MSQV) and the workflow embracing the Tree-structured Parzen Estimator (TPE). The optimized Categorical Boosting model by the D3 dataset (CatBoost-D3) could be considered the best classifier among the tree models, owning the higher accuracy (0.9352), precision (0.9348), recall (0.9352), F1-score (0.9350) and area under an receiver operating characteristics curve (AUC, 0.9730), and lower time cost. The best performances were associated with the ensemble effect of critical and secondary features, distinct from the previous finding which revealed only the effect of critical features on enhancing AUC value. Additionally, one observed that the present model was comparable to other optimized tree model by the dataset with double-class and outperformed the other tree model by the dataset with multi-class. The present work offers a new avenue for identifying hop trajectories and tracking sand particle flow via machine learning in the future, and a new channel for reunderstanding the relationship between midair collision and saltation under strong wind through automatic identification of saltating tracks.

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利用粒子瞬时速度的多个统计量自动识别高速视频中由强风驱动的盐渍化轨迹
由于盐化粒子跟踪算法(SPT)的准确率或召回率较低,强风驱动的盐化轨迹的演变情况仍不为人所知。由于效率低,人工识别盐化轨迹成为一个主要瓶颈,限制了高精度新型 SPT 的发展。在此,我们通过建立具有多个瞬时盐化速度统计量(MSQV)的数据集和包含树状结构帕尔森估计器(TPE)的工作流程,提出了一种优化的树状模型,用于自动识别强风下高速视频中的盐化轨迹。根据 D3 数据集优化的分类提升模型(CatBoost-D3)被认为是树状模型中最好的分类器,它具有更高的准确度(0.9352)、精确度(0.9348)、召回率(0.9352)、F1 分数(0.9350)和接收器工作特性曲线下面积(AUC,0.9730),以及更低的时间成本。最佳性能与关键特征和次要特征的组合效应有关,这与之前的发现不同,之前的发现只显示了关键特征对提高 AUC 值的影响。此外,我们还观察到,在双类数据集上,本模型与其他优化树模型不相上下,而在多类数据集上,本模型的表现优于其他树模型。本研究为今后通过机器学习识别跳跃轨迹和跟踪沙粒流提供了新的途径,也为通过自动识别盐化轨迹重新理解强风下半空碰撞与盐化之间的关系提供了新的渠道。
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来源期刊
Aeolian Research
Aeolian Research GEOGRAPHY, PHYSICAL-
CiteScore
7.10
自引率
6.10%
发文量
43
审稿时长
>12 weeks
期刊介绍: The scope of Aeolian Research includes the following topics: • Fundamental Aeolian processes, including sand and dust entrainment, transport and deposition of sediment • Modeling and field studies of Aeolian processes • Instrumentation/measurement in the field and lab • Practical applications including environmental impacts and erosion control • Aeolian landforms, geomorphology and paleoenvironments • Dust-atmosphere/cloud interactions.
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