3 papers
cs.CV2024
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis +1
Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences…
cs.LG2023
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting
Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson +1
The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficu…
cs.LG2022
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker +1
In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tas…