Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley
Bibliographic record
Abstract
Evaporation is influenced by several meteorological parameters, evaporation data are usually difficult to obtain compared to rainfall data, especially in arid regions. Developing a monthly evaporation prediction model in arid regions in terms of available meteorological data is a significant step. The data used in this study for modeling are monthly measurements to cover substantial continuity over a period of 18 years between January 2000 and December 2017. Stepwise and backward multiple linear regression techniques were used with a new procedure of variable selection to select the best model. Temperature, wind speed, relative humidity and sunshine hours were used as a independent variables in the multiple linear regression (MLR) technique to establish the best prediction of the evaporation model. To examine the MLR evaporation developed model in the current study, MLR results were compared with the most common evaporation models commonly used in arid regions such as Kharufa and Khosla methods. The results of performance indicators shows that the R2 values are approximately 0.937, 0.90 and 0.85 for MLR evaporation developed model, Kharufa and Khosla methods, respectively. Moreover, the values of the error measures, namely RMSE and NAE for MLR evaporation developed model were 36.3 and 0.123, Kharufa model 71.22 and 0.241 and Khosla model was and 173.7 and 0.581 respectively. Based on the foregoing, the results of the MLR developed evaporation model in the current study outperforms in all performance indicators and proves to be better than the Kharufa and Khosla models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".