An AI Powered Remote Sensing Framework for Monitoring and Predicting Roadside Water Quality

While air pollution is the most visible environmental impact of transportation systems, water pollution and quality issues are also of great importance in the transportation and environment nexus. Specifically, transportation systems can affect water quality directly in many ways, including stormwater runoff, deicing chemicals, vehicle exhaust, oil spills, and other pollutants. However, the impact of transportation systems on water qualify is not well studied or fully understood. A significant challenge is the slow movement of ground water through aquifers and its long-lasting, detrimental effects on communities, aquatic life, and the overall health of the ecosystem. Addressing this challenge requires continuous and reliable data collection, as well as advanced data analytics techniques. The traditional method of manual data sampling and analysis is not sufficient. In this project, we will design and develop an AI powered remote sensing framework and associated algorithm for roadside water quality monitoring and prediction. We expect this effort will enable opportunities for causality analysis based on long-term historical data, leading to better understanding of the root causes of water pollution and potential strategies for preserving the water quality.

Language

• English

Project

• Status: Active
• Funding: $120000
• Contract Numbers:

  69A3552348335

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Environmentally Responsible Transportation Center for Communities of Concern

  University of Missouri Kansas City
  Kansas City, Missouri  United States  64110
• Performing Organizations:

  Washington State University, Pullman

  Civil & Environmental Engineering Department
  PO Box 642910
  Pullman, WA  United States  99164-2910
• Principal Investigators:

  Shi, Xianming

  Zhao, Xinghui

  Zhang, Xuechen

  Gurocak, Hakan

• Start Date: 20230601
• Expected Completion Date: 20240531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Environmental monitoring; Remote sensing; Roadside; Runoff; Water pollution; Water quality
• Subject Areas: Data and Information Technology; Environment; Highways;

Filing Info

• Accession Number: 01895682
• Record Type: Research project
• Source Agency: Environmentally Responsible Transportation Center for Communities of Concern
• Contract Numbers: 69A3552348335
• Files: UTC, RIP
• Created Date: Oct 9 2023 2:18PM