15 citations · 25 across the 5 of their papers we have counts for
5 papers
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…
ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images
Yu-Hsuan Ho, Cheng-Chun Lee, Nicholas D. Diaz +2
We propose an automated lowest floor elevation (LFE) estimation algorithm based on computer vision techniques to leverage the latent information in street view images. Flood depth-…
Unveiling Vulnerability and Inequality in Disrupted Access to Dialysis Centers During Urban Flooding
Faxi Yuan, Hamed Farahmand, Russell Blessing +2
Despite the criticality of dialysis facilities, limited knowledge exists regarding the extent and inequality of disrupted access caused by weather events. This study uses mobility…
Examining Data Imbalance in Crowdsourced Reports for Improving Flash Flood Situational Awareness
Miguel Esparza, Hamed Farahmand, Samuel Brody +1
The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the po…
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…