A novel DEM enhancement methodology to improve physics-based susceptibility modeling of rainfall-induced landslide along anthropogenically modified slopes
Bibliographic record
Abstract
Rainfall-induced landslide is a pervasive hazard in steep, high-elevation terrains, where localized anthropogenic modifications implemented without adequate drainage systems increase pore water pressure, significantly exacerbating slope instability. The high expense and limited availability of fine-resolution data often hinders regional slope stability models from accounting for such fine-scale terrain details, leading to reduced predictive accuracy. This study presents a novel methodology to enhance digital elevation models (DEMs) for capturing fine-scale terrain features, focusing on terraced slopes. Using high-resolution satellite imagery, the approach integrates advanced image processing, particularly the Canny edge detection algorithm for precise terracing pattern recognition, combined with field measurements and geospatial analyses. Validation against LiDAR-derived DEMs yielded an R2 value of 0.9876, with extracted slopes exhibiting a mean relative error of 0.28, substantially outperforming conventional resampling methods. The framework integrating DEM enhancement and physics-based model was applied to two landslide case studies in the Western Ghats, India: 2021 Plappally and 2019 Kavalappara landslides. Results demonstrated this framework effectively delineates failure zones and captured critical pore pressure amplification due to terracing, aligning well with field observations and existing coupled flow-deformation analyses. This scalable methodology significantly enhances slope stability analyses and landslide hazard assessments in regions with anthropogenic slope modifications.
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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 | 0.000 | 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) | 0.001 | 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".