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

Area of Science:

• Environmental Science
• Geospatial Data Analysis
• Civil Engineering

Background:

• Sediment accumulation at culverts presents complex environmental challenges.
• Traditional experimental and physical modeling methods are often insufficient for addressing these large-scale processes.

Purpose of the Study:

• To develop a data-driven investigation into culvert sedimentation processes.
• To create a web-based geovisual analytics application for exploring sediment deposition at culverts.
• To identify key drivers and spatial patterns of culvert sedimentation.

Main Methods:

• Development of the IowaDOT platform, a web-based geovisual analytics application.
• Integration of analytical variables into a unified dataset.
• Quantification of culvert sedimentation using time-series aerial imagery.
• Identification of contributing drivers via a tree-based feature selection algorithm.
• Visualization of spatial and relational patterns using multivariate geovisualizations and Self-Organizing Maps (SOM).

Main Results:

• Identification of culvert sedimentation-prone regions within Iowa.
• Quantification of empirical relationships between identified drivers and the degree of culvert sedimentation.
• Positive user feedback from professional users regarding the platform's usefulness and satisfaction.

Conclusions:

• The developed data-driven approach and geovisual analytics platform offer effective insights into complex culvert sedimentation processes.
• The platform facilitates the identification of at-risk areas and the understanding of causative factors, aiding in environmental management and infrastructure maintenance.