Summary of Impact Of Employing Weather Forecast Data As Input to the Estimation Of Evapotranspiration by Deep Neural Network Models, By Pedro J. Vaz et al.
Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models
by Pedro J. Vaz, Gabriela Schütz, Carlos Guerrero, Pedro J. S. Cardoso
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the development of machine learning models for estimating Reference Evapotranspiration (ET0), a crucial parameter in smart irrigation scheduling. The authors propose using freely available weather forecast services (WFSs) as input features for these models, which can estimate ET0 up to 15 days in advance. To overcome the limitation of not having solar radiation (SR) as a free forecast parameter, the authors develop and evaluate two approaches: direct ET0 estimation by an artificial neural network (ANN) model and estimating SR using an ANN model and then computing ET0 using the FAO56-PM method. The study employs data from weather stations (WSs) and two online WFSs in Vale do Lobo, Portugal, finding that the latter approach achieves the best results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists develop new methods to predict how much water plants need based on weather forecasts. They want to use this information to create smart irrigation systems that can automatically provide plants with the right amount of water. The problem is that current weather forecast services don’t give us information about solar radiation, which is important for predicting evapotranspiration. To solve this problem, the researchers developed two different approaches to estimate evapotranspiration using machine learning models and freely available weather forecasts. They tested these methods with data from a weather station in Portugal and found that one of the approaches works better than the other. |
Keywords
* Artificial intelligence * Machine learning * Neural network
