Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management
| Autor: | Anna Botto, Claudio Paniconi, Jacques Gallichand, Silvio José Gumière, Matteo Camporese, Alain N. Rousseau, Jonathan A. Lafond |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Hydrus
010504 meteorology & atmospheric sciences business.industry 0207 environmental engineering 02 engineering and technology General Medicine Solver Machine learning computer.software_genre 01 natural sciences Water potential DNS root zone Richards equation Precision agriculture Artificial intelligence 020701 environmental engineering business Irrigation management computer Lead time 0105 earth and related environmental sciences |
| Zdroj: | Frontiers in Water. 2 |
| ISSN: | 2624-9375 |
| DOI: | 10.3389/frwa.2020.00008 |
| Popis: | 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 Quebec (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. |
| Databáze: | OpenAIRE |
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