Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header
Abstract
:1. Introduction
2. Theoretical Foundations of Evapotranspiration
Potential Evapotranspiration
- = reference evapotranspiration (mm/day).
- γ* = modified psychometric constant (mbar/°C).
- = saturation vapor pressure deficit (mb).
- = saturation vapor pressure (mb).
- = wind speed at 2 m from the surface (m/s).
- L = latent heat of vaporization (cal/g).
- ∆ = slope of the saturation pressure curve.
- γ = psychrometric constant (mbar/°C).
- = net radiation on the crop surface (cal/cm2 day).
- T = average temperature (°C).
- G = density of soil heat flux (cal/cm2).
- = reference evapotranspiration.
- = maximum temperature °C.
- = minimum temperature °C.
- = solar radiation extraterrestrial in (MJ/m2 day).
- = reference evapotranspiration.
- = solar radiation (MJ/m2 day).
- = maximum temperature °C.
- = minimum temperature °C.
- = is a coefficient that is calculated as follows:
- ET = reference evapotranspiration.
- RH = is the percentage relative humidity.
- T = average temperature °C.
3. Theoretical Foundations of Artificial Neural Networks
3.1. Artificial Neural Networks and Multilayer Perceptron
- = exit.= non-linear activation function.
- = bias (weights).
- = linear combination of inputs.
3.2. Multi-Layer Perceptron
3.3. Optimization
- W = new position for the parameters that are closest to the minimum.
- n = learning ratio
4. Materials and Methods
4.1. Data Description
4.2. KDD (Knowledge Discovery from Data)
4.3. The Data Science and Its Application in Hydrology
4.4. Study Area
4.5. Used Approaches
4.6. Method Applied to Avoid Prediction
4.7. Performance Measures of Predict Models
5. Results and Discussion
5.1. Results
5.1.1. Data Pre-Processing Results
5.1.2. Applied Methods Results
5.2. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Ref. | Equation |
---|---|---|
Penman-Monteith equation | [25] | (1) |
Hargreaves-Samani equation | [22] | (2) |
Ritchie equation | [23] | (3) |
Turc equation | [24] | (4) |
(5) |
Indicator | Neural Network | Penman-Monteith | Hargreaves-Samani | Ritchie | Turc |
---|---|---|---|---|---|
MAE | 0.033 | 0.586 | 0.467 | 0.749 | 0.104 |
MSE | 0.002 | 0.411 | 0.285 | 0.632 | 0.016 |
RMSE | 0.043 | 0.641 | 0.534 | 0.795 | 0.128 |
RAE | 0.016 | 0.230 | 0.192 | 0.285 | 0.046 |
R-Squared | 0.998 | 0.550 | 0.689 | 0.309 | 0.982 |
t-7 | t-6 | t-5 | t-4 | t-3 | t-2 | t-1 | t |
---|---|---|---|---|---|---|---|
−0.32 | −0.32 | −0.33 | 0.35 | −0.35 | −0.36 | −0.36 | 0.35 |
−0.32 | −0.33 | −0.35 | 0.35 | −0.36 | −0.36 | −0.35 | 0.36 |
−0.33 | −0.35 | −0.35 | 0.36 | −0.36 | −0.35 | −0.36 | 0.36 |
−0.35 | −0.35 | −0.36 | 0.36 | −0.35 | −0.36 | −0.36 | 0.36 |
−0.35 | −0.36 | −0.36 | 0.35 | −0.36 | −0.36 | −0.36 | 0.37 |
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Pino-Vargas, E.; Taya-Acosta, E.; Ingol-Blanco, E.; Torres-Rúa, A. Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header. Agriculture 2022, 12, 1971. https://doi.org/10.3390/agriculture12121971
Pino-Vargas E, Taya-Acosta E, Ingol-Blanco E, Torres-Rúa A. Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header. Agriculture. 2022; 12(12):1971. https://doi.org/10.3390/agriculture12121971
Chicago/Turabian StylePino-Vargas, Edwin, Edgar Taya-Acosta, Eusebio Ingol-Blanco, and Alfonso Torres-Rúa. 2022. "Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header" Agriculture 12, no. 12: 1971. https://doi.org/10.3390/agriculture12121971