Improved Landslide Deformation Prediction Using Convolutional Neural Network–Gated Recurrent Unit and Spatial–Temporal Data
Bibliographic record
Abstract
As one of the major forms of geological disaster, landslides cause huge casualties and economic losses in China every year. Given the importance of landslide prediction, it is a challenging task due to difficulties in efficiently leveraging the spatial–temporal information for enhanced prediction. This paper presents a novel spatial–temporal enhanced CNN-GRU model to improve landslide predictions with the following contributions. First, this paper explicitly models the spatial correlation in the dataset and constructs a spatial–temporal time-sequence deformation prediction model that greatly improves landslide predictions. This model integrates the spatial correlation of monitoring points into time-series deformation prediction to improve the prediction of landslide deformation trends. Second, we develop a complete data processing pipeline involving SBAS-InSAR, time-series data preprocessing, spatial–temporal homogeneous point selection and weighting, as well as CNN-GRU model training. The pipeline is tailor-designed to leverage the spatial–temporal correlation in the data to enhance the prediction performance. Third, we apply the proposed model to monitor landslide deformation around Woda Village, Chamdo City, Tibet. The results show that the root mean square error (RMSE) of the monitoring points in the landslide area is reduced by about 20.9% and the number of points with an RMSE of less than 3 mm is increased by 12.9%, leading to a significant improvement in prediction accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".