Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley
Why this work is in the frame
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Bibliographic record
Abstract
The use of traditional methods to predict evaporation may face many obstacles due to the influence of many factors on the pattern of evaporation's shape. Therefore, the use of existing methods of artificial intelligence is a reliable prediction model in many applications in engineering. Monthly measurements were employed in the present work to predict for duration eighteen years, from beginning of January 2000 until December 2017. The best model was chosen using ANNs (MLP, RBF) and AI (SVM) techniques. The best evaporation model prediction was made using ANNs (MLP, RBF) and AI (SVM) technologies, with temperature, wind speed, relative humidity, and sunshine hours used as independent variables. Several statistical metrics have been used to evaluate the effectiveness of the proposed model to other popular artificial intelligence models. The obtained result denotes the superiority of the MLP models over the RBF and SVM models. It is concluded that the MLP model is better than RBF and SVM for evaporation prediction for both groups. A comparison of the model performance between MLP, RBF, and SVM models indicated that the MLP-ANN method presents the best estimates of monthly evaporation rate with minimum RMSE 0.033, minimum MAE 0.026, and maximum determination coefficient 0.967.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it