A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Haowen Xu; Ibrahim Demir; Caglar Koylu; Marian Muste

doi:10.1016/j.scitotenv.2019.07.157

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A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Journal article

Peer reviewed

A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Haowen Xu, Ibrahim Demir, Caglar Koylu and Marian Muste

Science of the Total Environment, Vol.692, pp.806-817

11/20/2019

DOI: 10.1016/j.scitotenv.2019.07.157

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https://www.osti.gov/biblio/1649492View

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Abstract

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. [Display omitted] •A geovisual analytics platform is designed for exploring culvert sedimentation contributors.•Several computational and visual analytics techniques are integrated into the web platform.•The platform helps users identify culvert sedimentation-prone regions in Iowa.•The platform improves the understanding of complex patterns in culvert sedimentation processes.•The platform design is generalizable and adaptable to other environmental studies.

Erosion and sedimentation

Web-based

Geovisual analytics

Environmental transport processes

Culverts

Data-driven

GIS application

Multivariate clustering

Decision support

Details

Title: Subtitle: A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation
Creators: Haowen Xu - Computational Urban Sciences Group, Oak Ridge National Laboratory, United States of America
Ibrahim Demir - IIHR—Hydroscience & Engineering, the University of Iowa, United States of America
Caglar Koylu - Geographical and Sustainability Sciences, the University of Iowa, United States of America
Marian Muste - IIHR—Hydroscience & Engineering, the University of Iowa, United States of America
Resource Type: Journal article
Publication Details: Science of the Total Environment, Vol.692, pp.806-817
DOI: 10.1016/j.scitotenv.2019.07.157
ISSN: 0048-9697
eISSN: 1879-1026
Publisher: Elsevier B.V
Grant note: name: Iowa Highway Research Board; DOI: 10.13039/100014791, name: Iowa Department of Transportation, award: TR-655
Language: English
Date published: 11/20/2019
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Center for Social Science Innovation; Injury Prevention Research Center; Public Policy Center (Archive); Geographical and Sustainability Sciences; Mechanical Engineering
Record Identifier: 9983983648002771

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