A random forest machine learning model to detect fluvial hazards
Bibliographic record
Abstract
Abstract Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.
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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 | 0.001 |
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".