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Found 5 papers, 3 papers with code
Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation
This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods.
CloudNine: Analyzing Meteorological Observation Impact on Weather Prediction Using Explainable Graph Neural Networks
Combining an XGNN-based atmospheric state estimation model with a numerical weather prediction model, we provide a web application to search for observations in the 3D space of the Earth system and to visualize the impact of individual observations on predictions in specific spatial regions and time periods.
Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network
By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance.
Ranked #1 on
Solar Irradiance Forecasting
on ASOS Data
Learning multi-resolution representations of research patterns in bibliographic networks
This study aims at representing research patterns of bibliographic entities (e. g., scholars, papers, and venues) with a fixed-length vector.
Ranked #1 on
Research Performance Prediction
on AMiner
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?
Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles.
