LCFSTE: Landslide Conditioning Factors and Swin Transformer Ensemble for Landslide Susceptibility Assessment
Bibliographic record
Abstract
Landslide susceptibility assessment (LSA) holds crucial importance in guiding regional disaster prevention and reduction efforts. However, current deep learning (DL) models for LSA encounter challenges like insufficient landslide data samples and uneven distribution. In this paper, we develop a new hybrid framework named LCFSTE, which integrates landslide conditioning factors (LCFs) and Swin Transformer (Swin-T) for LSA. With this framework, we fully leverage the powerful nonlinear feature extraction capability of Swin-T to extract abstract features from both landslides and LCFs. This approach ultimately enhances the precision and reliability of LSA. To assess the performance of our newly proposed framework, we selected Jiuzhaigou County, China, as our study area. Firstly, a dataset for LSA was constructed using historical landslide data and 11 multi-source LCFs. Then, these factors were screened through multicollinearity test and factor importance analysis using variance inflation factors, tolerance, and information gain rate. Subsequently, the dataset was divided into three subsets: 60% for training, 20% for validation and 20% for testing. Then, the LSA results were compared with four DL models. Seven evaluation metrics (EMs) are chosen to quantitatively evaluate the performance of these five LSA models. The results demonstrated that, among these seven EMs, LCFSTE outperformed the others, achieving the highest score in six out of the seven considered EMs. This outcome highlights the promising applicability of LCFSTE in enhancing LSA accuracy.
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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 | 0.000 |
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".