Spatial modeling and mapping of riverbank erosion through the integration of machine learning application and in situ data
Bibliographic record
Abstract
Assessing riverbank erosion is crucial for identifying hazard zones and implementing protective measures from potential disasters. Traditional in situ measurements, though effective, are often costly and time-consuming for large-scale evaluations. This study proposes a methodology integrating machine learning (ML) applications to assess riverbank erosion across an entire area using existing submerged jet erosion test (JET) measurements. In situ JETs were used to measure the bank erosion rate at each site, identify influencing factors, and randomly split datasets for training and testing. Four ML techniques, random forest (RF), decision trees, multiple linear regression, and gradient boosting regressor, are applied to establish the correlation between riverbank erosion and its influencing factors. The RF model demonstrated the highest accuracy ( R 2 = 0.94, root-mean-squared error = 3.04, and Nash–Sutcliffe efficiency = 0.93) among all algorithms, and the optimal model was applied to predict and map annual erosion rates, which were validated with additional submerged JET data. The proposed methodology can effectively model and map riverbank erosion in similar settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 source (direct Gemma or distilled Codex), 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".