Focal Interest
Focal Interest

Hydrological Forecasting Using AI

External reference: https://openalex.org/T11490

  1. Self-supervised graph model improved multi-horizon weather forecasts
  2. Neural network predicts shifts in extreme weather frequency
    Neural networks leverage climate model data to predict how extreme rainfall, hail, and winds will shift geographically as climate changes, accounting for terrain effects.
  3. Graph neural networks identified flood-vulnerable river segments
    Graph neural network framework for assessing flood vulnerability in river basins. Identifies high-risk segments and flood-prone sub-basins by combining hydrological attributes with network topology.
  4. Hydrological ML accuracy depends on training data quantity and quality
    Analysis of how information quantity and quality in training data affect machine learning prediction accuracy for hydrological variables, using information theory and mechanistic model integration.