Research
Senior Honors Thesis: Runoff Forecasting with Deep Learning
Post-processing operational streamflow forecasts with Hybrid Transformer/RNN models to improve accuracy while respecting physical constraints.
Architecture
Hybrid Transformer + RNN residual correction
Text-based diagram of the modeling pipeline.
- Inputs: Operational NWM forecasts, meteorological forcings, static attributes.
- Encoder: Transformer capturing multi-scale temporal context.
- Integration: Fusing physical model outputs with deep learning corrections.
- Pipeline: Automated feature normalization and leakage-safe splitting.
Evaluation
Metrics and validation strategy
Emphasis on leakage-safe splits and operational realism.
Metrics
- Metrics: RMSE, NSE, KGE (Hydrology-standard metrics).
- Validation: Spatial holdouts to test generalization.
- Diagnostics: Analyzing error distributions across seasons.
Reproducibility
- Config-driven experiments (Hydra) with fixed seeds.
- Strict data versioning and artifact tracking.
- Full environment captures for every run.
Constraints
Real-world considerations
Operational limitations that shaped model design and evaluation.
- Must run within operational latency limits.
- Robustness to missing sensor data.