Dépôt numérique

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management.


Téléchargements par mois depuis la dernière année

Plus de statistiques...

Gumière, Silvio José, Camporese, Matteo, Botto, Anna, Lafond, Jonathan A., Paniconi, Claudio ORCID: https://orcid.org/0000-0003-2063-2841, Gallichand, Jacques et Rousseau, Alain N. ORCID: https://orcid.org/0000-0002-3439-2124 (2020). Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management. Frontiers in Water , vol. 2 . DOI: 10.3389/frwa.2020.00008.

[thumbnail of P3703.pdf]
Télécharger (1MB) | Prévisualisation


Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error.

Type de document: Article
Mots-clés libres: machine learning; physics-based model; soil water dynamics; irrigation management; precision agriculture; random forest
Centre: Centre Eau Terre Environnement
Date de dépôt: 24 juill. 2020 13:35
Dernière modification: 15 févr. 2022 20:46
URI: https://espace.inrs.ca/id/eprint/10333

Actions (Identification requise)

Modifier la notice Modifier la notice