Comparative Analysis of Local and Transferred ANN Models in Landslide Susceptibility Prediction in a Tropical Region
Bibliographic record
Abstract
Landslides are a common form of natural disaster in the tropics due to heavy rainfall in the wet season.Due to the hazards that come with landslides, determining the susceptibility of an area is of utmost importance.Currently, this is done through an MLbased approach.However, some areas may lack the required data.Thus, this study focused on comparing the impact of a transferred ML model from a comprehensive data region to a localized model.This was done by developing an ANN model trained on data from Western Sarawak and comparing it to the localized model in the west coast of Sabah and Selangor.The transferred ANN model results were acceptable, with recall scores of 0.89 and 0.86 for the west coast of Sabah and Selangor, respectively, while the localized models both achieved a recall score of 1. AUC scores were also comparable, at 0.988 and 0.995 for the west coast of Sabah and Selangor, respectively, while the localized models both achieved an AUC of 1.For the LSMs, in both target areas, the transferred ANN model predictions were heavily skewed in comparison to the localised model.It is recommended that future studies test the transferability in other tropical regions beyond Southeast Asia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".