Autonomous Discovery of Unified Multi-Hazard Prediction Theory from Netatmo IoT Weather Big Data Using AlphaEvolve
Year: 2026
Type: report
Abstract: Accurate multi-hazard disaster prediction across convective storms, earthquakes, and volcanic eruptions is essential for reducing societal vulnerability. Here, I investigate whether an artificial intelligence agent can autonomously discover a unified, predictive physical theory from scratch. Leveraging Google DeepMind's AlphaEvolve and 175,363 crowdsourced Netatmo IoT sensor readings across Japan, the agent iteratively explored Python algorithmic representations. Rather than fragmented heuristics, AlphaEvolve formulated three foundational physical principles: the Continuous Enthalpy Potential Field, Kinematic-Barometric Orthogonality, and Dynamic Semblance Scaling. When synthesized into an executable script, the unified theory successfully predicted all 21 historical disaster events with 100% sensitivity, zero false alarms during control periods, and up to 84.9% extended lead times, demonstrating the autonomous genesis of rigorous physical theories.
Author: Tanaike, Kanshi
Institution: Independent Research Association
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221761809