The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction
Bibliographic record
Abstract
Accurate landslide displacement prediction is an essential component of landslide early warning systems. The Bazimen landslide in the Three Gorges reservoir area was taken as a case study. In recent years, the outstanding performance of dynamic and static models has attracted the attention of many scholars. Static machine learning algorithm—support vector regression (SVR) and dy-namic machine learning algorithm—long short-term memory neural network (LSTM) were used to predict landslide displacement in this study. At first, to predict landslide displacement accu-rately, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Second, by introducing the K-Nearest Neighbor (KNN), the forecast results of SVR and LSTM models were classified with greater accuracy based on the se-lection of input factors. Finally, we proposed a static-dynamic ensemble landslide displacement predic-tion model optimized with KNN to address the dynamic characteristics of landslide evo-lution and the shortcomings of traditional static prediction models. Through the out-put of the KNN model, the results of the static-dynamic coupling model are updated to get the KNN-optimized SVR-LSTM landslide displacement prediction model. Compared with traditional landslide displacement prediction models that output prediction values, this study optimized the prediction results through a classification algorithm. The results show that the LSTM models show better performance than the SVR models: For monitor-ing point ZG111, the values of RMSE and MAPE with the SVR model were 30.71 mm and 2.15%, while the accuracy factors of the LSTM model were 24.73 mm and 1.87% and the values of RMSE and MAPE with the Ensemble model were 23.11 mm and 1.68%. It can be seen that the ensemble model integrates the advantages of static (SVR) and dynamic (LSTM) models, and its prediction performance is better than that of the single SVR model and the LSTM model. This study provides an integrated view of the landslide displace-ment pre-diction model, which can provide a reference for predicting geological disasters in the Three Gorges reservoir area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".