Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential
Bibliographic record
Abstract
There are a large number of dams throughout the United States, and a considerable portion of them are categorized as having high hazard potential. This state of affairs constitutes a challenge, especially when coupled with their rapid deterioration. As such, this research paper proposes an optimized data-driven model for the fast and efficient prediction of dam hazard potential. The proposed model is envisioned on two main components, namely model development and model assessment. In the first component, a hybridization of the differential evolution algorithm and regression tree to forecast downstream dam hazard potential is proposed. In this context, the differential evolution (DE) algorithm is deployed to: (1) automatically retrieve the optimal set of input features affecting dam hazard potential; and (2) amplify the search mechanism of regression tree (REGT) through optimizing its hyper parameters. As for the second component, the developed DE-REGT model is validated using four folds of comparative assessments to evaluate its prediction capabilities. In the first fold, the developed DE-REGT model is trialed against nine highly regarded machine learning and deep learning models. The second fold is designated to structure, an integrative ranking of the investigated data-driven models, counting on their scores in the performance evaluation metrics. The third fold is used to study the effectiveness of using differential evolution for the hyper parameter optimization of regression tree. The fourth fold aims at testing the usefulness of using differential evolution as a feature extractor algorithm. Performance comparative analysis demonstrated that the developed DE-REGT model outperformed the remainder of the data-driven models. It accomplished mean absolute percentage error, relative absolute error, mean absolute error, root squared error, root mean squared error and a Nash–Sutcliffe efficiency of 9.62%, 0.27, 0.17, 0.31, 0.41 and 0.74, respectively. Results also revealed that the developed model managed to perform better than other meta-heuristic-based regression tree models and classical feature extraction algorithms, exemplifying the appropriateness of using differential evolution for hyper parameter optimization and feature extraction. It can be argued that the developed model could assist policy makers in the prioritization of their maintenance management plans and reduce impairments caused by the failure or misoperation of dams.
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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".