6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features
Cheng-Chun Lee, Lipai Huang, Federico Antolini +4
Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates a propose…
physics.soc-ph2021★ 6 cited
Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data
Faxi Yuan, William Mobley, Hamed Farahmand +5
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood…