Predictive Modeling of Daily Evapotranspiration in Arid Regions Using Artificial Neural Networks
Bibliographic record
Abstract
For thousands of years, people have understood the importance of evapotranspiration (ET) in maintaining the hydrologic cycle and replenishing the world's freshwater supplies.The process of estimating evapotranspiration with a high accuracy in arid regions is considered one of the most important processes in hydrological studies.It has a great importance in the efficient management of water resources and hydrological modeling, as well as in the management of irrigation operations, because these areas are permanently linked to the issue of water scarcity.The prediction of evapotranspiration is a vital step towards management of water resource.This study aims to develop an artificial neural network model to predict the evapotranspiration in arid regions.The RBFNN and GRNN models with six input data were used in present study.The input data are Max.temperature, Min.temperature, Ava.Temperature, Humidity, Wind speed and Solar radiation.The ANN modelling was achieved by using MATLAB with hyperbolic sigmoid transfer function for both input and output layers.Several statistical indicators have been used for examining the model's prediction accuracy.Results show that the current model is a powerful model which has the capability to predict the evapotranspiration with high accuracy.The superiority of the GRNN model is very obvious in comparison to the RBFNN model, where the coefficient of determination for GRNN model was more than 96% in comparison with 94% for RBFNN and the mean square error for GRNN was 0.4 in comparison with 0.52 for RBFNN model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".