Integration of multicriteria evaluation and cellular automata methods for landslide simulation modelling
Bibliographic record
Abstract
The representation and modelling of physical and natural systems are mostly implemented using partial differential equations. Physical processes such as flooding, tsunamis, avalanches, earthquakes and landslides undeniably possess complex systems behaviour. Modelling such dynamic phenomena can be adequately addressed by using geocomplexity – complex systems theory and cellular automata (CA). This study develops a novel approach that couples geographic information systems (GIS), multicriteria evaluation (MCE) and CA to simulate shallow landslide flows occurring in urban areas. The landslide susceptibility map produced from the MCE model was used as one input for the CA model. The high-resolution digital elevation model (DEM) is used to calculate topographic variables such as slope gradient, aspect gradient, stream power index, topographic wetness index, and flow direction which were all used in model design. The developed MCE-CA simulation model was tested on historical landslide data in Metro Vancouver, Canada. The spatial extents of the landslide simulations were compared with actual data to test the model simulation outcomes. The developed model has a potential to become useful tool that can aid urban planners and emergency workers in identifying and mitigating threats due to landslides.
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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 | 0.001 | 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".