Context-Aware Temporal Deep Learning for Basin-Integrated River Water Level Time Series Imputation

Puntos clave

• The aim is to enhance river water level forecasting by developing a model that imputes missing values in time series data.
• Applied a context-aware deep learning model adapted from clinical time series data.
• Learned interrelationships between water levels and rainfall in the same basin.
• Focused on improving imputation for missing values in river water level data.
• The proposed model effectively imputes missing water level data.
• Demonstrated improved prediction accuracy over traditional methods.
• Enhanced understanding of the relationship between rainfall and river levels.