2 citations · 4 across the 4 of their papers we have counts for
5 papers
Multi-Quantile Regression for Extreme Precipitation Downscaling
Hamed Najafi, Gareth Lagerwall, Jayantha Obeysekera +1
Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that drive flood risk. We demonstrate…
Uncovering Insights of Compound Flooding with Data-Driven AI
Xu Zheng, Chaohao Lin, Sipeng Chen +7
Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approache…
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Zichuan Liu, Tianchun Wang, Jimeng Shi +7
Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series s…
FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation
Jimeng Shi, Zeda Yin, Arturo Leon +2
In coastal river systems, frequent floods, often occurring during major storms or king tides, pose a severe threat to lives and property. However, these floods can be mitigated or…
Deep Learning Models for Flood Predictions in South Florida
Jimeng Shi, Zeda Yin, Rukmangadh Myana +5
Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. Physics-based detailed hydrological a…