1 citations · 2 across the 7 of their papers we have counts for
7 papers
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…
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…
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +5
Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on…
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…
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…
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…
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…