Runzi Wang, PhD, is a transdisciplinary researcher who studies change in natural and urban environments across space and over time, with the objective to drive positive change with ecological planning and design strategies. Combining technologies such as big data, machine learning, remote sensing, and spatial statistics, her primary research explores how land cover change and urban development pattern influence stream water quality and stormwater quality at the watershed basis, together with a variety of environmental, climatic and sociocultural factors. By enhancing the interpretability of machine learning in its application to landscape architecture, the most innovative part of her research is to uncover the nonlinear relationships between environmental, technological and sociocultural dimensions of landscape systems.
The focus areas of Runzi’s research include 1) Neighborhood-scale green infrastructure design optimization. 2) Regional-scale water quality management. 3) Continental-scale landscape change monitoring and projection. Runzi is also a landscape designer engaged in evidence-based landscape and urban design projects, with the focus on stormwater management. She integrates research with the teaching topics of ecological design studio, landscape analysis, and site engineering.
Peer-reviewed Journal Articles
Wang, R., Kim, J. H., & Li, M. H. (2021). Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach. Science of The Total Environment, 761, 144057.
Song, Y., Wang, R., Fernandez, J., & Li, D. (2021) Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches. Landscape and Urban Planning, 205, 103956.
Wang, R., Zhang, X., & Li, M.-H. (2019). Predicting bioretention pollutant removal efficiency with design features: A data-driven approach. Journal of Environmental Management, 242, 403–414. doi: 10.1016/j.jenvman.2019.04.064
Selected Peer-reviewed Conference Abstract
Wang, R. & Zhao, G. (2020). The synergistic effects of climate variability, streamflow, land cover change, and fertilizer application on long-term stream water quality across scales. American Geophysical Union (AGU) fall meeting
Guzman, C. B., Wang, R., Muellerklein, O., Eager., C., & Smith, M. (2020). Identifying relationships between urban stormwater signatures and watershed characteristics using interpretable machine learning . American Geophysical Union (AGU) fall meeting.
Wang, R., Zhao, G., & Li, M. H. (2018). Developing longitudinal models of 20-year water quality in Texas gulf region—A hybrid of remote sensing and machine learning approach. American Geophysical Union (AGU) fall meeting, Washington D. C.
2021.6-2022.1 | Urban water quality management towards a sustainable framework— the investigation of fine scale urban form effects on stream water quality. Graham Sustainability Institute Catalyst Grant Program. $10,000, Principal Investigator.
2018.6-2020.6 | Identifying Socio-Environmental Watershed Typologies Based on Stormwater Pollution Using Machine Learning. The National Socio-Environmental Synthesis Center (SESYNC) Graduate Pursuit. $10,000, Co-principal Investigator.
Michigan State University (PhD, Environmental Design) 2018-2020
Texas A&M University (Urban and Regional Sciences) 2014-2017
Peking University (MS, Landscape Architecture) 2011-2013
Shandong University (BE, Architecture) 2006-2011