36 citations · 49 across the 20 of their papers we have counts for
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The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
Jimeng Shi, Vitalii Stebliankin, Giri Narasimhan
Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By…
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang +2
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of ext…
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…
Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
Jimeng Shi, Rukmangadh Myana, Vitalii Stebliankin +2
Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applic…