Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.1.1. Geography, Water Resources and Climate
2.1.2. Available Data
2.2. Artificial Neural Networks
2.2.1. Basic Theory and Architecture
2.2.2. Data Pre-Processing
2.2.3. Training Algorithms
2.3. Sensitivity Analysis
2.4. Validation and Statistical Assessment
3. Results and Discussion
3.1. ANNs Modeling Results
3.2. Model Generalizability
3.3. Agreement with Past Studies
3.4. Comparisons with Conventional Evaporation Estimation Methods
3.5. Model Shortcomings
4. Conclusions
Funding
Conflicts of Interest
References
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Meteorological Variable | Mean | Standard Deviation | Minimum | First Quartile | Median | Third Quartile | Maximum |
---|---|---|---|---|---|---|---|
Max. Temperature, Tmax (°C) | 34.0 | 10.5 | 9.0 | 24.2 | 35.3 | 44.0 | 51.5 |
Min. Temperature, Tmin (°C) | 19.6 | 8.8 | −1.6 | 12.1 | 20.4 | 27.4 | 39.7 |
Avg. Temperature, Tavg (°C) | 27.0 | 9.6 | 5.1 | 18.1 | 27.9 | 36.3 | 44.1 |
Max. Relative Humidity, RHmax | 56.2 | 27.2 | 9.0 | 30.0 | 55.0 | 82.0 | 100.0 |
Min. Relative Humidity, RHmin | 18.5 | 15.5 | 0.0 | 7.0 | 12.7 | 25.0 | 95.0 |
Avg. Relative Humidity, RHavg | 37.4 | 20.2 | 5.5 | 19.0 | 34.2 | 53.5 | 97.5 |
Avg. Wind Speed, W (m/s) | 4.1 | 1.9 | 0.1 | 2.6 | 3.8 | 5.2 | 11.5 |
Pan Evaporation, Epan (mm) | 11.2 | 7.6 | 0.1 | 4.8 | 9.6 | 16.3 | 40.0 |
ANN Model No. | Meteorological Variable Combination |
---|---|
Model 1 | Tavg |
Model 2 | Tavg and W |
Model 3 | Tavg and RHavg, |
Model 4 | Tavg, W, and RHavg |
Model No. | Statistical Metric | Training Period | Validation Period |
---|---|---|---|
Model 1 | Pearson correlation | 0.882 | 0.825 |
R2 | 0.778 | 0.681 | |
NS | 0.778 | 0.405 | |
MAE (mm) | 2.771 | 3.609 | |
Model 2 | Pearson correlation | 0.922 | 0.913 |
R2 | 0.85 | 0.833 | |
NS | 0.807 | 0.755 | |
MAE (mm) | 2.517 | 2.155 | |
Model 3 | Pearson correlation | 0.907 | 0.862 |
R2 | 0.822 | 0.742 | |
NS | 0.817 | 0.509 | |
MAE (mm) | 2.403 | 3.284 | |
Model 4 | Pearson correlation | 0.937 | 0.93 |
R2 | 0.879 | 0.864 | |
NS | 0.871 | 0.638 | |
MAE (mm) | 2.017 | 2.920 |
Model Parameter | Value |
---|---|
Input layers | 2 |
Hidden layers | 10 |
Output layers | 1 |
Training algorithms | Levenberg-Marquardt |
Number of epochs | 8 |
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Alsumaiei, A.A. Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates. Water 2020, 12, 1508. https://doi.org/10.3390/w12051508
Alsumaiei AA. Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates. Water. 2020; 12(5):1508. https://doi.org/10.3390/w12051508
Chicago/Turabian StyleAlsumaiei, Abdullah A. 2020. "Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates" Water 12, no. 5: 1508. https://doi.org/10.3390/w12051508