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20232026
most citedMaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features

1 citations · 2 across the 7 of their papers we have counts for

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cs.LG2026

Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models

Lipai Huang, Adithi Srinath, Manas Singh +2

Near-real-time flood depth prediction demands surrogate models that are accurate, fast, and transferable across watersheds. Supervised surrogates can match physics-based simulators…

cs.LG2026

Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcasting

Lipai Huang, Kai Yin, Chia-Fu Liu +1

We propose R2RAG-Flood, a training-free retrieval-augmented generation framework for flood damage nowcasting with reinforced reasoning. The framework builds a reasoning-centric kno…

cs.LG2024

High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data

Lipai Huang, Federico Antolini, Ali Mostafavi +3

High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated dat…

cs.LG2024

FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation

Chia-Fu Liu, Lipai Huang, Kai Yin +2

Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed…

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…

cs.LG2023★ 1 cited

FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation

Zhewei Liu, Lipai Huang, Chao Fan +1

Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fiel…