Seasonality-Aware Meta-Learning Framework for Improving Rainfall Prediction Accuracy in Data-Scarce Environments

Key Points

• Rainfall prediction accuracy improves with a new meta-learning framework, demonstrating significant enhancements.
• Using only 10 data points, the framework achieved excellent predictive performance in experiments across 22 regions.
• The approach incorporates seasonality into loss functions to accurately model summer rainfall variability.
• This solution could potentially address data scarcity challenges in hydrological time series forecasting.

Abstract