Xinghui Zhao | Washington State University

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Presentation Description

While air pollution is the most visible environmental impact of transportation systems, water pollution and water quality issues are also of great importance in the transportation and environment nexus. However, how the transportation sector impacts the water quality of adjacent receiving water bodies is not fully understood or well studied.

In this talk, we present our work in designing and developing a cost-effective, remote sensing framework to automate water quality monitoring (WQM), and incorporate data analytics and machine learning technologies to facilitate the informative data-driven decision-making process. While current machine learning models excel at predicting future outcomes based on historical data, they often fall short in terms of understanding the underlying causal mechanisms driving these trends. To gain a deeper understanding on the environmental impact of transportation systems, a causality based analysis is critical. To this end, we have performed extensive data analysis utilizing data from the National Water Quality Monitoring Council and the National Oceanic and Atmospheric Administration (NOAA). We have analyzed the causal effect of various variables including precipitation and conductivity to better understand the potential effects of road runoff and other activities that can affect the water when it rains.

Our research represents a significant step towards applying modern AI solutions to gain a deeper understanding of the role we play in the environmental change of our local communities. By combining remote sensing platforms with causal data analysis, we aim to better and more easily understand the hidden causal relationships between each variable. Our work has the potential to help mitigate the negative impact on the environment by providing guidelines on system design and policy making, leveraging the causal relations learned from the historical data.

Speaker Biography

Dr. Xinghui Zhao is the Director of the School of Engineering and Computer Science at Washington State University Vancouver. She joined WSU Vancouver in 2012, and previously received a Ph.D. in Computer Science from University of Saskatchewan in Canada. Dr. Zhao has extensive experience in conducting research in parallel and distributed systems, machine learning, and big data computing. She is particularly interested in interdisciplinary research projects which leverage cutting edge machine learning and AI technologies to solve large scale, real-world problems. Dr. Zhao is an active member of IEEE, ACM, ASEE, and IEEE Women in Engineering. She has been taking leadership roles in the professional communities to organize multiple international conferences, such as IEEE Cluster, IEEE/ACM Utility and Cloud Computing (UCC), and IEEE/ACM Big Data Computing, Applications and Technologies (BDCAT). She also serves as an editor for Journal of Future Generation Computer Systems.

Co-presenters

Anais Barja
Washington State University

Xianming Shi
Washington State University

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