Cascade Detection Engine
Predicting what comes next
When a major disaster strikes, it rarely ends with the first event. Our cascade detection engine analyzes every threat against peer-reviewed scientific rules to identify secondary hazards before they materialize.
How It Works
From raw data to predictive intelligence
Monitor
Continuous ingestion from 220+ authoritative sources. Every seismic event, volcanic eruption, wildfire, and cyclone is captured within minutes of detection.
Analyze
Each event is evaluated against 20 peer-reviewed cascade rules. Spatial proximity, temporal windows, and magnitude thresholds determine which secondary hazards are plausible.
Predict
Identified cascade risks are propagated through multi-level chains. A magnitude 7.0 earthquake near a coast can trigger tsunami warnings, which in turn flag coastal flooding zones.
Cascade Families
Six categories of hazard interaction
Seismic Cascades
5 interactionsVolcanic Cascades
2 interactionsFire Cascades
2 interactionsCyclonic Cascades
2 interactionsHydrological Cascades
4 interactionsSpace & Systemic Cascades
5 interactionsSpatial & Temporal Decay
How distance and time shape risk
Spatial Decay
Cascade likelihood decreases with distance from the triggering event. A landslide is far more probable near the epicenter than hundreds of kilometers away. Our spatial decay model captures this relationship, weighting nearby regions more heavily than distant ones.
Temporal Decay
Secondary hazards have time windows. Tsunamis follow earthquakes within minutes, landslides within hours, and disease outbreaks within days to weeks. The temporal decay model ensures alerts remain relevant without persisting indefinitely.
Chain Prediction
Multi-level cascade propagation
Cascade detection goes beyond direct cause-and-effect. When an earthquake triggers a tsunami, the system then evaluates whether that tsunami could cause coastal flooding or infrastructure damage — creating a chain of predicted hazards up to three levels deep.
Scientific Foundation
Built on peer-reviewed research
Gill, J.C. & Malamud, B.D. (2014)
Reviewing and visualizing the interactions of natural hazards.
Reviews of Geophysics, 52(4), 680-722.
Framework for multi-hazard interaction classification
Kappes, M.S., Keiler, M., von Elverfeldt, K. & Glade, T. (2012)
Challenges of analyzing multi-hazard risk: a review.
Natural Hazards, 64(2), 1925-1958.
Multi-hazard risk assessment methodology
Pescaroli, G. & Alexander, D. (2018)
Understanding compound, interconnected, interacting, and cascading risks: a lesson from COVID will not teach us.
Risk Analysis, 38(11), 2245-2257.
Cascading and compound risk analysis framework
See cascade detection in action
Open the live map to see real-time cascade alerts across the globe.