A Systematic Review of Neural Network Applications for Groundwater Level Prediction
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
This systematic review investigates the application of neural networks (NNs) for groundwater level (GWL) prediction. The study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique to screen and synthesize relevant data, focusing on input variables, data size, and performance metrics. The results indicate a growing preference for hybrid models, which are effective in capturing hidden relationships between GWL and environmental factors. The root mean square error (RMSE) emerges as the predominant performance metric, highlighting its significance in evaluating NNs. The incorporation of lagged values is identified as crucial for enhancing predictive accuracy. In conclusion, this systematic review provides a concise overview of NN applications in GWL prediction, emphasizing the efficacy of hybrid models and the importance of RMSE as a performance metric. The findings contribute to the understanding of trends in groundwater research, addressing both technical nuances and broader environmental challenges.
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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) | 0.002 | 0.001 |
| 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 | 0.001 |
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 it