Prediction of pan evaporation across diverse climates and scenarios using temporal attention clockwork recurrent neural networks coupled with long short-term memory
Bibliographic record
Abstract
Accurate prediction of evaporation is crucial for effective water resource management, particularly in regions facing water scarcity. This study investigates evaporation dynamics at two distinct locations with different climates in Iran (Minab and Ramsar stations) by employing machine learning methods, including simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), ClockWork Recurrent Neural Network (CWRNN), Hybrid temporal attention RNN-LSTM, and CWRNN-LSTM models under 11 different scenarios. Our findings include: (1) The temporal attention CWRNN-LSTM model achieved R 2 values of 0.982 at Minab and 0.985 at Ramsar during testing, indicating a strong correlation between predicted and observed evaporation data; (2) The model produced low Root Mean Square Error (RMSE) values of 0.412 and 0.255, respectively, reflecting its high accuracy; (3) In comparison, conventional models showed lower performance, with improvements of up to 32.4% in R 2 and 65.7% in RMSE measures for the hybrid model over its standalone counterparts. By capturing underlying trends and variations in evaporation dynamics, the temporal attention CWRNN-LSTM model demonstrates its applicability in informed decision-making for water resource management. • Utilizes advanced hybrid deep learning techniques, such as temporal attention CWRNN-LSTM, for enhanced evaporation prediction. • Achieves R 2 values of 0.982 and 0.985 at Minab and Ramsar, respectively, demonstrating high accuracy. • Analyze the advanced hybrid deep learning techniques for two different climates stations and 11 different scenarios • Enhances understanding of climatic factors influencing evaporation predictions. • Provides practical insights for sustainable water resource management amid climate change challenges.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".