A Framework of Systematic Land Use Vulnerability Modeling Based on Seismic Microzonation: A Case Study of Miri District of Sarawak, Malaysia
Bibliographic record
Abstract
The objective of this research is to develop the framework for sustainable land-use planning on the basis of seismic microzonation to reduce the devastating effects of future earthquakes by utilizing the software geographical information system (ArcGIS). Miri district of Sarawak in Malaysia has been chosen as the study area because of having the highest peak ground acceleration which is 0.09g in terms of the 10% probability of exceedance in 50 years. In addition, the frequency of an earthquake with a magnitude up to 5.3 is approximately every 5-7 years. Therefore, it is vital to introduce land use planning in order to diminish the adverse effects of earthquakes in the future. For this purpose, Google Earth Pro was used for the collection of satellite image data for land use planning purposes. From the results, it was found that the seismic hazard in the Miri district varies from low to high corresponding to 2475 years of return period with low to moderate as predominant over the Miri district. Only a few areas are under high hazard. Also, the land use planning map was compared with the current land use map acquired from satellite imagery and it was found that all built-up is in the low hazard area. It is envisaged that the findings from this research will contribute immensely to the literature that will serve as background information and a guide for analysts, disaster management, engineering designers and seismologists in Malaysia and the world as a whole.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".