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Water Luncheon Seminar - Machine Learning Insight for Rapid Flood Inundation Screening
Water Luncheon Seminar
Hosted by Ohio Floodplain Management Association
Machine Learning Insight for Rapid Flood Inundation Screening
By Mark Bartlett and Jeff Albee
Rapid, regional scale riverine and pluvial flood risk assessment and forecasting is complicated by hydrology and hydraulic complexity. Complexity is not fully captured by the most detailed of hydrology models—causing model results to deviate from observations over long time scales. Moreover, as the spatial extents (i.e., scale) and resolution of the study area increase, the associated traditional hydraulic models become computationally expensive. Accordingly, traditional hydrology and hydraulic modeling seemingly is at odds with the efficiency needed for regional flood analysis. Here, we show that an effective, rapid prediction of pluvial flood inundation is achieved through a direct statistical simulation of hydrologic averages coupled to the insight of a machine learning model that extends high-resolution detailed HEC-RAS 2D model results over regional areas. We found that the direct statistical simulation of the watershed water balance provides a reliable baseline of the watershed moisture status (in comparison to satellite data) and so provides for a reasonable estimation of runoff. This runoff estimation is then mapped to inundation extents based on the machine learning process. While the machine learning accuracy is around 80-percent in comparison to the detailed HEC-RAS 2D models (based on high-resolution digital elevation models), we show the results are more reasonable and detailed when compared to previous mapping efforts. For two case study areas, our results demonstrate how machine learning can provide insights from data whether it be remote sensing or model derived. We anticipate that similar machine learning approaches will start to complement traditional hydraulic modeling efforts in rapidly extending results to large regions. As machine learning approaches evolve, we foresee data driven machine learning approaches capturing more of the complex functional dynamics of flood modeling without the parameter uncertainty of traditional modeling approaches.
Wednesday, June 16, 2021, 12:00 PM until 1:00 PM
Hosted by the Ohio Water Resources Center and the Water Management Association of Ohio.
Registration is required
Payment in Full In Advance Or At Event
Mark Bartlett is the Engineering Insight & Analytics Technical Lead at Stantec where he has innovated engineering analytics for extracting data insights that expedite the modeling process. Mark is an expert in climate change, hydraulic and hydrologic modeling, stormwater management, agricultural and ecohydrological modeling, and the application of probability and statistics in science and engineering. Mark's research agenda advances the study of hydro-climate variability, surface and subsurface hydrology, stochastic (i.e., random) processes, and ecohydrology - the interdisciplinary analysis of the interactions between water and ecosystems. In 2016, he became a fellow in the National Institute of Food and Agriculture of the United States Department of Agriculture (USDA) and was awarded funding for a two-year project that compares the benefits of C4, and crassulacean acid metabolism (CAM) photosynthesis cultivation on semi-arid, marginal lands for biofuel production.
Jeff Albee works in the architecture, engineering, and construction (AEC) industry as a trailblazer in digital engineering for over two decades. He is focused his career on understanding technology trends and their influence on the development of future engineering successes in automation, technology-as-a-service, artificial intelligence, and digital asset management. Smart cities, building information modeling, digital twin, artificial intelligence, and more. As our world becomes inundated with data, digital technology is simply the price of admission for doing business. Jeff is passionate about bringing people back into that equation—in delivering technology focused on how we use it. Additionally, as a leader in Stantec’s Innovation Office, Jeff and his team support ideas with digital components that help our clients, communities, and company.