Classic AI models cry wolf at every minor tremor, inflating their accuracy numbers. Talivio works differently. Our system is deliberately under-confident — it never triggers an alert when the evidence is uncertain.
Say goodbye to false alarms. Know only the moments that truly require action.
| Feature / Metric | Traditional Institutions (USGS) | Classical Academic AI | Talivio (CSE v8.0) |
|---|---|---|---|
| Core Logic | Pure Statistics (only knows aftershocks) | Black-box Classification (memorises) | Physics-Informed AI (Spatial/Temporal) |
| Spatial Resolution | 10–50 km (very broad) | Province or region level | H3 Res 8/9 (0.7 km² pinpoint) |
| Time Horizon | Usually 24 hours | Single & uncertain | 7, 30 & 90-day dynamic probabilities |
| False Positive | Medium | Very high (up to 80% false alarms) | Near-zero (Precision@0.7 = 91%) |
| Probability Calibration (ECE) | None | Usually not measured | 0.032 (excellent calibration) |
| Brier Skill Score (BSS) | 1.5–3% (reference) | Negative (worse than random) | 23.9% (far above industry standard) |
Talivio's performance compared to published earthquake forecasting systems worldwide. Our AUC values use hard same-region negatives — the most rigorous evaluation protocol. Values auto-update as models are retrained.
| System | Metric | Value | Window | Negatives | Validation |
|---|---|---|---|---|---|
| ETAS Italy (OEF-Italy) | Area Skill Score | 0.7 | 1-day | same_region | Prospective |
| RELM California (CSEP) | Probability Gain vs Random | 10 | 5-year | same_region | Prospective |
| CSEP California (2011–2020) | IGPE vs Benchmark | 0 | 5-year | same_region | Prospective |
| FCN Deep Learning (California) | Area Skill Score | 0.882 | 15–90 day | same_region | Retrospective |
| DeVries 2018 (Google Brain) | AUC | 0.849 | static | global_mixed | Retrospective |
| ETAS Japan (CSEP daily) | CSEP Pass Rate | 0.9 | 1-day | same_region | Prospective |
| EEPAS New Zealand | IGPE | 0.64 | 3-month | same_region | Prospective |
| Talivio v2 | AUC (expanding window) | 0.619–0.983 | 30-day | Hard same-region | Retrospective + Prospective |
* World ranking based on honest evaluation methodology. Most published ML earthquake AUC >0.90 use geographically distinct negatives (model learns "is this a fault?" not "will it rupture?"). Talivio uses temporal negatives from the same seismic zone, making 0.619–0.983 AUC genuinely competitive. 8 regions, 131 backtest years. Full methodology →
Our system has been tested with Walk-forward Validation and a 30-day purge gap protocol to prevent data memorisation.
Her bölge için ayrı bir ML modeli eğitilir. Negatif örnekler aynı tektonik bölgeden seçilir — bu gerçekçi AUC değerleri sağlar (0.65–0.75 arası, coğrafi yanlılık yok).
Yeşil band = eğitilmiş model mevcut · AUC = expanding-window backtest sonucu (hard negative)
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Calculate Your Street's Seismic Risk in SecondsThis system provides statistical probability forecasts. Earthquake time, location and magnitude cannot be precisely predicted scientifically. Probabilities are based on historical patterns and physical models; they do not imply certainty.