Computational Urban Sciences Group, Oak Ridge National Laboratory, United States of America.
IIHR-Hydroscience & Engineering, the University of Iowa, United States of America.
Sci Total Environ. 2019 Nov 20;692:806-817. doi: 10.1016/j.scitotenv.2019.07.157. Epub 2019 Jul 11.
Sediment accumulation at culverts involves large-scale and interlinked environmental processes that are difficult to address with experimental or physical modeling methods. This article presents an alternative data-driven investigation for shedding insights into these processes. Accordingly, a web-based geovisual analytics application, the IowaDOT platform, was developed, which allows users to explore the complex processes associated with the sediment deposition at culverts. The platform provides systematic procedures for (1) collecting and integrating analytical variables into a single dataset, (2) quantifying the degree of culvert sedimentation using time series of aerial images, (3) identifying drivers that contribute to culvert sedimentation processes from a variety of culvert structural and upstream landscape characteristics using a tree-based feature selection algorithm, and (4) facilitating the understanding of complex spatial and relational patterns of culvert sedimentation processes using multivariate geovisualizations supported by a self-organizing map (SOM). As the outcomes of this study, these patterns identify culvert sedimentation-prone regions in Iowa and quantify empirical relationships between the drivers and culvert sedimentation degrees. A simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedbacks are received.
涵管中的泥沙淤积涉及大规模且相互关联的环境过程，这些过程很难通过实验或物理建模方法来解决。本文提出了一种替代的数据驱动研究方法，以深入了解这些过程。为此，开发了一个基于网络的地理可视化分析应用程序，即爱荷华州交通部平台，该平台允许用户探索与涵管泥沙淤积相关的复杂过程。该平台提供了系统的程序，用于 (1) 将分析变量收集并整合到单个数据集中，(2) 使用航空图像的时间序列量化涵管淤积的程度，(3) 使用基于树的特征选择算法从各种涵管结构和上游景观特征中识别导致涵管淤积过程的驱动因素，以及 (4) 使用自组织映射 (SOM) 支持的多元地理可视化来促进对涵管淤积过程复杂空间和关系模式的理解。作为本研究的结果，这些模式确定了爱荷华州容易发生涵管淤积的地区，并量化了驱动因素与涵管淤积程度之间的经验关系。通过专业用户对该平台进行了简单评估，以评估该工具的有用性和用户满意度，收到了积极的反馈。
