Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning
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View abstract on PubMed
Summary
This summary is machine-generated.Physics-Informed Neural Networks (PINNs) improve reference evapotranspiration (ETo) estimation by integrating semi-physical models into AI. This enhances accuracy and trustworthiness for better agricultural water management.
Area Of Science
- Environmental Science
- Agricultural Engineering
- Artificial Intelligence
Background
- Reference evapotranspiration (ETo) is crucial for agriculture, but standard methods like the Penman-Monteith equation are data-intensive.
- Existing simplified models have limitations, and purely data-driven AI methods raise concerns about explainability and reliability.
- There is a need for accurate, trustworthy, and data-efficient ETo estimation techniques.
Purpose Of The Study
- To evaluate the integration of semi-physical (SP) models into the loss function of Physics-Informed Neural Networks (PINNs) for ETo estimation.
- To assess the impact of combining data-driven and physics-based losses on model performance across various data availability scenarios.
- To enhance the explainability and reliability of AI models for ETo estimation in environmental applications.
Main Methods
- Developed a novel PINN approach combining a data-driven loss with losses from SP models (Priestley-Taylor, Makkink, Hargreaves-Samani, Abtew) using a convex combination parameter (θ).
- Collected in-situ agrometeorological data (air temperature, solar radiation, relative humidity, wind speed) from Morocco.
- Calibrated SP models using CMA-ES optimization and trained/evaluated the PINN under different data scenarios and θ values.
Main Results
- The integrated PINN approach significantly improved ETo estimation accuracy (RMSE, R²) compared to fully data-driven or baseline models across all tested data scenarios.
- The PINN required less training time to reach optimal values when θ was within the [0.2, 0.8] range.
- Optimal θ values were determined for each SP model and data scenario, demonstrating the flexibility of the approach.
Conclusions
- Physics-Informed Neural Networks offer a promising, accurate, and trustworthy method for reference evapotranspiration estimation.
- Integrating physical constraints into AI models enhances their reliability and applicability in operational environmental management.
- This study represents a significant step towards developing controlled, physics-informed AI for environmental science applications.
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