Application of multi-criteria decision-making approach using TOPSIS to identify the vulnerable time zone of earthquake time series signal
Bibliographic record
Abstract
Conventional analysis of time series signals representing earthquakes does not provide any clue about the vulnerability of such disastrous events. Time series signals contain P and S waves, which can detect earthquake epicentres. Due to the failure of the old method for determining earthquake susceptibility over time, decision-making is needed. This research suggests a multi-criteria decision-making method to determine earthquake signal risk time zones. This study used TOPSIS for this job. TOPSIS ranks greatest and worst resemblance to positive and negative ideal solutions. Alternatives and criteria constitute the decision matrix. Segmenting the earthquake's duration creates alternate time zones, and seismic signal dynamics are used to set criteria. Statistical mean and standard deviation are two criteria among many. Other criteria include Hurst exponent, power spectrum maximum amplitude, and segmented signal anomaly (assumed as alternate). The proposed approach was tested using Indian Meteorological Department Bhuj earthquake data. The paper describes how to evaluate criteria for a time zone alternative. To simplify computation, earthquake incidence is separated into 14 equal-length time segments. Results demonstrate that the proposed method accurately detects earthquake time series signal sensitive time zones.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".