Spatiotemporal deep learning approach for estimating water content profiles in soil layers
Bibliographic record
Abstract
Land subsidence associated with using natural groundwater resources for serving the growing population needs has been receiving extensive research attention in the literature over the past few decades. The water content fluctuation in the of subsurface soil layers significantly impacts the land subsidence. The key objective of this study is to predict changes in water content profiles in soil layers over a long period of time using a deep learning-based approach. A convolution neural network algorithm that is commonly used in Artificial Intelligence (AI) applications is modified in the present study for processing in-situ measurement water content profiles. The approach used in the proposed AI method has a distinct advantage for generating dynamic predictions based on the extracted spatiotemporal characteristics of the data. In addition, three different algorithms are compared with respect to time series prediction, including long-short-term memory (LSTM), multiple-layer perceptron (MLP) networks and autoregressive integrated moving average (ARIMA).
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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".