Integrated Analysis Framework for Predicting Surface Runoff, Infiltration, and Slope Stability
Bibliographic record
Abstract
Buildup of pore-water pressure due to water infiltration during an extreme rainfall event is a major cause of many landslides worldwide. A competent simulation model can greatly contribute to the reliable landslide prediction and prevention. Infiltration is transient, dependent on subsurface conditions, surface runoff characteristics, and rainfall hyetograph. However, conventional analysis commonly relies on the assumptions of infiltration rate and/or water table location, which cannot be determined reliably a priori. In this paper, an integrated analysis framework for prediction of surface runoff, infiltration, pore water pressure, and geomechanical stresses is presented. A fully coupled groundwater-surface water interaction numerical model, HydroGeoSphere, is used to compute the transient surface runoff and subsurface pore-water pressure responses due to rainfall simultaneously without the need to make assumptions about the infiltration rate. Therefore, rainfall hyetograph can be used directly as an input parameter in the numerical model. The computed pore-pressure as a function of time is used as input to slope stability analysis using finite element methods. A simplified example based on a full-scale instrumented slope in Hong Kong is presented to illustrate the integrated framework. The subsurface profile, soil properties, and boundary conditions were taken from the data obtained from a site investigation. A range of rainfall conditions was analyzed to evaluate the validity of some of the assumptions commonly made in conventional analysis approach.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".