Extreme Sea-Level Deep Learning Model
Team
This project builds a deep-learning architecture that detects non-stationarity in coastal storm surge and predicts extreme sea-level events across 208 US tide-gauge stations, combining climate data science with extreme value theory.
From this research area
- A methodological framework for improving the performance of data-driven models (2023)
- Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees (2018)
- Design of a web-based application of the coupled multi-agent system model and environmental model for watershed management analysis using Hadoop (2015)
- Groundwater Modeling with MODFLOW as a Web Application. (2015)