Assessment of pan coefficient performance: A comparative study of empirical and model-driven approaches using a hill-climbing-based alternating model tree and MOORA
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Bibliographic record
Abstract
The Maritime Provinces of Canada play a significant role in the country's agricultural productivity, yet they face numerous changes due to climate change. Therefore, a reliable estimation of reference evapotranspiration (ET o ) requires accurate determination of the pan coefficient (K pan ). However, this is quite challenging due to variations in climate change and the deep non-linearity of meteorological data. Intensive experiments for pan evaporation (E pan ) were conducted to develop a model, which includes hill-climbing based BestFirst-ClassifierSubsetEval (BF), alternating model tree (AMT), and multi-objective optimization by ratio analysis (MOORA). The model was assessed by comparing its performance using Bidirectional long-short-term memory (Bi-LSTM), recurrent neural network (RNN), random forest (RF), elastic regression net (Elastic net), and Instance-based learner K-Nearest Neighbor (IBK). The model was further evaluated using five empirical equations of FAO-56. The input data included seven daily meteorological variables, including maximum, minimum, mean, relative humidity, Wind, and Slope, extracted from 2018 to 2023 datasets to compute ET o and K pan locally measured E pan . Statistical indicators, including correlation coefficient (R), root mean square error (RMSE), Kling–Gupta efficiency (KGE), and Vulnerability, evaluated the model output. SHAP (Shapley Additive exPlanations) and Individual Conditional Expectation (ICE) were used to interpret the models' flexibility and visualize complex geographical phenomena and processes in an RF model. Overall, the outcomes revealed that the primary model (BF-AMT) outperformed all the data-driven and empirical models in terms of optimal metrics (RMSE=0.0143, Vulnerability=6.3260, and MOORA=0), followed by BF-Elastic net (RMSE=0.7891, Vulnerability=28.1081, and MOORA=0.073) and BF-Bi-LSTM (RMSE=0.0169, Vulnerability=64.8649, and MOORA=0.128), respectively. Finally, the SHAP results showed that wind and relative humidity were the most influential factors affecting the pan coefficient values. • An experimental study on Maritime Canada area for accurately estimating evapotranspiration. • Advanced ATM ensemble model with BestFirst, and MOORA to estimate more precise pan coefficients. • Comparing main scheme with BF-bi-LSTM, BF-RNN, BF-RF, BF-IBK, and BF-Elastic net. • Provided a pathway to improve water use efficiency and enhance irrigation practices.
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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 | 0.000 | 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 | 0.001 |
| 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