Improving groundwater level forecasting with a feedforward neural network and linearly regressed projected precipitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A module that uses neural networks was developed for forecasting the groundwater changes in an aquifer. A modified standard Feedforward Neural Network (FNN), trained with the Levenberg–Marquardt (LM) algorithm with five input variables (precipitation, temperature, runoff, groundwater level and specific yield) with a deterministic component, is used. The deterministic component links precipitation with the seasonal recharge of the aquifer and projects the seasonal average precipitations. A new algorithm is applied to forecast the groundwater level changes in Messara Valley, Crete, Greece, where groundwater level has been steadily decreasing due to overexploitation during the last 20 years. Results from the new algorithm show that the introduction of specific yield improved the groundwater level forecasting marginally but the linearly projected precipitation component drastically increased the window of forecasting up to 30 months, equivalent to five biannual time-steps.
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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 | 0.001 |
| 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