Cluster-Based Hydrologic Prediction Using Genetic Algorithm-Trained Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Most hydrological processes are nonlinear in nature. Although there have been many successful applications of artificial neural networks (ANNs) to capture these nonlinear relationships, there are cases when ANNs have not been able to predict flow extremes (low and high flows) accurately. In a more general sense, ANNs have not performed well when data are clustered. The objective of this study is to demonstrate the influence of clustering on neural network performance by constructing a cluster-based conjunction model based on clustering, neural networks, and genetic algorithm (GA). The performance of the GA-trained cluster-based model is compared to that of the Bayesian regularization back-propagation algorithm, the Levenberg–Marquatrdt algorithm, and a regular GA-trained ANN model. The cluster-based neural network model was tested on (1) chaotic time series data (the Henon map); (2) cross-correlated monthly streamflow data. Results from the study indicate that the cluster-based neural network model offers a promising alternative to its conventional counterparts in mapping fragmented input–output relationships. From threshold analysis it is found that the cluster-based neural network model was effective, compared to its counterparts, in capturing the dynamics of high flows. Improvement in clustering accuracy is shown to improve the performance of the cluster-based neural network model.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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