Disaster Prediction Architecture

Read this first (updated 2026-09-04). This paper mixes what runs today with where the architecture is headed; the two are marked throughout.

Live today: 16 hazard feeds (USGS, NWS, NIFC, NOAA GOES, FEMA, Cal OES) ingested every few minutes onto one public map · watcher agents that pre-screen those feeds into watch areas for human review · Trinetra's own multi-domain risk forecasts — earthquake, fire, flood, tornado and hurricane — each carrying a probability, severity and confidence score over a forecast window (typically 72 hours), shown on the live map under the "✦ Predictions" layer, where each forecast carries a ✦ badge that visually distinguishes it from a confirmed, observed event — alongside externally issued forecasts such as NWS cones and USGS products · a dedicated 72-hour fire-behaviour propagation forecast · nine-domain seasonal outlooks in the admin console.

What Trinetra does not do: it issues no public alerts of its own — it displays the official ones — and it never dispatches, tasks, or directs responders or resources. Every incident screen reads "Informational only — not directive. Always follow official orders."

Rows and sections marked design goal below describe intent, not delivered capability.

Project Trinetra — From Early Warning to Deep Prediction

Overview

Trinetra's disaster prediction capability moves beyond reactive alerting to proactive forecasting. Using a fleet of specialized AI agents, multi-modal data fusion, and time-series modeling, the system anticipates disasters before they are officially triggered by government APIs.

The goal: give communities and responders actionable intelligence before disaster strikes, not after.

The Agent Fleet

Trinetra operates a swarm of specialized AI agents, each responsible for a specific disaster domain:

Multi-Modal Fusion Analysis

Each agent ingests multiple data streams simultaneously to build a predictive picture:

• Atmospheric & Satellite Fusion — Rather than waiting for a fire hotspot, the system analyzes satellite imagery for vegetation dryness (fuel load) combined with localized wind patterns to predict high-risk ignition zones.

• Predictive Hydrology — The Flood Watcher doesn't just monitor stream gauges; it analyzes upstream rainfall rates and soil saturation to predict inundation levels hours before they happen.

• Seismic Slow-Slip Monitoring — The Prophet Agent analyzes raw USGS slow-slip data to provide a "Probability of Impact" window, enabling pre-emptive resource staging.

• Zero-Party Data (ZPD) Mesh (concept — not implemented) — Barometer readings and environmental data from user devices create a hyper-local prediction mesh, crowdsourcing atmospheric conditions at block-level granularity.

Prediction Pipeline

1. Data Ingestion

Real-time feeds from 16+ government and scientific APIs (USGS, NOAA, NWS, NASA FIRMS, ShakeAlert, DART buoys) are continuously ingested and normalized.

2. Agent Analysis

Each domain agent independently processes its data streams using on-device AI models (Liquid AI LFM) and cloud-based vision models for multi-modal analysis.

3. Predictive Consensus

When multiple agents identify converging risk signals, they negotiate a Probability Score via the Agent-to-Agent (A2A) coordination protocol. For example:

• If the Flood Watcher predicts 70% flood risk AND the Forecaster predicts heavy rainfall, the combined probability triggers an ActionPlan before any water hits the ground.

4. Pre-emptive Mandates

Retracted 2026-08-29. An earlier version of this section said the Supervisor Agent issues resource-positioning directives above a confidence threshold. Trinetra does not and will not do this: the standing doctrine is never dispatch, task, or direct. When agents' signals converge, the Supervisor flags a watch area for a human to review; any request to involve responders goes to a human review queue and is never executed autonomously. Trinetra issues no public alerts of its own — it displays official alerts.

5. Truth Ledger Verification

Every prediction is cryptographically logged on the Truth Ledger with:

• Confidence interval and justification
• Data sources and timestamps
• SHA-256 hash for future accuracy auditing

This creates an immutable record that can be verified retroactively — did the prediction match the outcome?

16 Disaster Domains

Trinetra monitors 16 distinct disaster domains, each with domain-specific severity scales:

Sovereign AI Architecture

Trinetra's prediction models run on a three-tier hybrid AI architecture:

Tier 1 — On-Device (Mobile)

• Liquid AI LFM 1.2B (697 MB GGUF) on iOS/Android
• Sub-second inference for local hazard assessment
• Cache-first: the map, coach, and inspection drafts work offline; cloud is used for photo analysis unless a sovereign deployment is configured

Tier 2 — Sovereign Hardware (DGX Spark)

• NVIDIA Grace Blackwell GB10 with 128 GB unified memory
• Custom EMBER fine-tuned models for property compliance
• Available as a deployment profile for customer-operated environments; the public app runs on Google Cloud (Gemini) today

Tier 3 — Cloud (Fallback)

• Cloud Run with GPU acceleration
• Gemini Flash for high-throughput analysis
• Used only when sovereign capacity is exceeded

Privacy-First Prediction

• All prediction data is aggregated and anonymized at the community level
• Individual device sensor data stays on-device unless explicitly shared
• The Truth Ledger records prediction outcomes, not personal data
• On-device AI processing means property photos never leave the resident's phone

Trinetra — Predictive Stewardship for Community Resilience Copyright 2026 Unravel The Love. All rights reserved.