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Hydrologically constrained genetic programming for interpretable rainfall–runoff model discovery: A process–informed machine learning approach
A journal article published by Elsevier on 2026-11-01 presents a machine-learning method that constrains genetic programming with hydrological knowledge to produce interpretable rainfall–runoff models.
Categories: science-and-space, environment-and-climate
Generated scores
Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.
- Confidence
- 6/10
- Geographic reach
- 3/10
- Global importance
- 3/10
- Impact magnitude
- 3/10
- Positivity
- 7/10
- Urgency
- 2/10
Why it matters
The method aims to improve discovery of interpretable hydrological models, which is relevant to hydrology research and model development.

