The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: ACM Student Research Competition, Undergraduate
Author: Lucas Raicu (University of Chicago), Daniel Grzenda (University of Chicago), Ian Foster (University of Chicago), Kyle Chard (University of Chicago)
Supervisor: Kyle Chard (University of Chicago)
Abstract: Accurate forecasting of water levels is essential for flood mitigation. Traditionally, predictions have been based on harmonic analysis and sensor networks maintained by the National Oceanographic and Atmospheric Administration. However, these methods struggle with high-variance events that change water levels from the long-term tidal baseline. TidalMark evaluates the ability of a variety of deep learning models to model these high-variance events. Through extensive hyperparameter sweeps and comparisons across model variants, we have evaluated trade-offs in accuracy, generalization, and scalability. Our results show that properly tuned machine learning models consistently outperform the scientific-standard harmonic approaches between 2.1X and 4.7X (between one- to seven-day predictions) with the goal towards achieving adaptive, scalable, and accurate forecasting of coastal water levels.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF