AlphaEvolve and Gemini: Autonomous Algorithm Discovery for Unified Disaster Prediction

Description and use

AlphaEvolve and Gemini autonomously generate transparent mathematical algorithms for micro‑scale disaster prediction using Netatmo data, achieving 100 % detection and 0 % false alarms.

Problem

Accurately predicting micro‑scale natural disasters such as localized convective downpours, inland earthquakes, and volcanic eruptions is hampered by sparse governmental observation networks and the complexity of human‑crafted algorithms.

Outcome

AlphaEvolve autonomously discovered a unified multi‑hazard prediction theory from Netatmo IoT data, achieving perfect detection and zero false alarms across 21 historical disasters, and producing a transparent white‑box algorithm that can be interpreted as a physical theory.

Workflow

• 1. Feed 175,363 real disaster observations into AlphaEvolve.
• 2. AlphaEvolve autonomously writes, mutates, and refines explicit Python mathematical algorithms.
• 3. Generated algorithms are assembled into a high‑speed deterministic multi‑hazard prediction model.
• 4. Model achieves 100 % detection and 0 % false alarms across 21 historical disasters.
• 5. Transparent algorithm is read and elevated into a unified geophysical theory.

Evidence

AlphaEvolve autonomously writes, mutates, and refines explicit Python mathematical algorithms without human threshold tuning.

Source

Those autonomously generated algorithms directly construct a high‑speed, deterministic multi‑hazard prediction model that achieves 100% detection across 21 historical disasters with 0.0% false alarms.

Source

Because the AI‑created algorithm is 100% transparent white‑box mathematics, we can directly read its code and elevate it into a unified geophysical theory governing torrential rainstorms, inland earthquakes, and volcanic eruptions.

Source