Artificial Neural Networks Approximation of Density Dependent Saltwater Intrusion Process in Coastal Aquifers
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Bibliographic record
Abstract
The flow and transport processes in a coastal aquifer are highly nonlinear, where both the flow and transport processes become density dependent. Therefore, numerical simulation of the saltwater intrusion process in such an aquifer is complex and time consuming. An approximate simulation of those complex flow and transport processes may be very useful, if sufficiently accurate, especially where repetitive simulations of these processes are necessary. A simulation methodology using a trained artificial neural network model (ANN) is developed to approximate the three-dimensional density dependent flow and transport processes in a coastal aquifer. The data required for initially training the ANN model is generated by using a numerical simulation model (FEMWATER). The simulated data consisting of corresponding sets of input and output patterns are used to train a multilayer perceptron using the back-propagation algorithm. The trained ANN predicts the concentration at specified observation locations at different times. The performance of the ANN as a simulator of the density dependent saltwater intrusion process in a coastal aquifer is evaluated using an illustrative study area. These evaluation results show that the ANN technique can be successfully used for approximating the three-dimensional flow and transport processes in coastal aquifers.
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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 | 0.002 | 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 it