Travis Adrian Dantzer

Travis Adrian Dantzer

PhD Candidate in Intelligent Systems Engineering

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

talks

Real-time control of dissolved oxygen in a stormwater network

Published:

Short abstract. Real-time control may enable stormwater networks to manage stormwater pollution and create optimal conditions for aquatic life. The limited number of real-world case studies has hindered the acceptance and adoption of these autonomous solutions. In particular, no studies have explored the in-situ, real-time control of water quality directly. To that end, we present a novel dissolved oxygen monitoring and control system deployed in a municipal stormwater wetland in Ann Arbor, Michigan, US. This study shows how dissolved oxygen levels are influenced by real-time control, and we discuss the implications of the results to future system-level control studies.

Automatically discovering interpretable rainfall-runoff models

Published:

Short abstract. Hydrologic sensor networks are expanding rapidly and generating more data every day. As the volume and resolution of these data sets grow, manual analysis, model calibration and quality control become infeasible. Few existing approaches can transform raw data into interpretable results. Process-based models are not designed to consume large amounts of real-time data, while machine learning models are typically uninterpretable, limiting insight and trust. To address this gap of automation and interpretability, we present a scalable approach that discovers differential equations and latent state estimations in water systems using only rainfall and runoff measurements. This method generates approximations of watersheds as nonlinear, time invariant dynamical systems automatically from measurements. We capture rainfall-runoff relations for catchments and combined sewers of all scales using between five and thirty parameters. We also demonstrate the method’s potential for surrogate modeling by replicating the dynamics of a large process-based model at a small fraction of the computational complexity. This parsimonious representation of watershed dynamics provides theoretical insight and the computational efficiency to enable automated predictions across large sensor networks.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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