A Methodological Proposal Based on Artificial Neural Networks for Evapotranspiration Assessment
Bibliographic record
Abstract
Evapotranspiration is the combined process in which water is transferred from the soil by evaporation and through the plants by transpiration to the atmosphere. Therefore, it is a central parameter in Agriculture since it expresses the amount of water to be returned by irrigation. Aiming to standardize Evapotranspiration estimate, the term “reference crop evapotranspiration (ETo)” was coined as the rate of Evapotranspiration from a hypothetical grass surface of uniform height, actively growing, completely shading the ground and well watered. ETo can be measured with lysimeters or estimated by mathematical approaches. Although, Penman-Monteith FAO 56 (PM) is the recommended method to estimate ETo by PM, it is necessary to register maximum and minimum temperatures (ºC), solar radiation (hours), relative humidity (%) and wind speed (m/seg.). Some of these parameters are missing in the historical meteorological registers. Here, Artificial Neural Networks (ANNs) can aid traditional methodologies. ANNs learn, recognise patterns and generalise complex relationships among large datasets to produce meaningful results even when input data is wrong or incomplete. The target of this study is to assess ANNs capability to estimatie ETo values. We have built and tested several architectures guided by Levenberg-Marquardt algorithm with 5 above mentioned parameters as inputs, from 1 to 50 hidden nodes and 1 parameter as output. Architectures with 10, 15 and 20 nodes in the hidden layer brought outsanding r2 values: 0.935, 0.937, 0.937 along with the highest intercept and the lowest slope values, which demonstrate that ANNs approach was an afficient method to estimate ETo.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".