River Network

River networks are complex systems crucial for water resource management and ecological health, with research focusing on improving prediction and monitoring capabilities. Current efforts utilize deep learning models, including graph neural networks (GNNs), to forecast water levels and detect anomalies in sensor data from river systems, offering significantly faster alternatives to traditional physics-based models. These advancements are improving flood prediction accuracy and efficiency, enhancing water quality monitoring, and ultimately supporting better decision-making for water resource management and environmental protection.

Papers

May 30, 2024

The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
High Performance River Network Coastal Flood Prediction

June 28, 2023

Deep Learning Models for Flood Predictions in South Florida
Jimeng Shi, Zeda Yin, Rukmangadh Myana, Khandker Ishtiaq, Anupama John, Jayantha Obeysekera, Arturo Leon, Giri Narasimhan
Deep Learning Model Physic Based Model Flood Prediction River Network

April 19, 2023

Graph Neural Network-Based Anomaly Detection for River Network Systems
Katie Buchhorn, Edgar Santos-Fernandez, Kerrie Mengersen, Robert Salomone
Anomaly Detection Graph Neural Graph Network River Network

River Network

Papers

The Merit of River Network Topology for Neural Flood Forecasting

Deep Learning Models for Flood Predictions in South Florida

Graph Neural Network-Based Anomaly Detection for River Network Systems