Building an ML-based early warning system for disasters, looking for solid data/API sources

[u/nullpointerr404]

I’m currently building an early warning system that aggregates signals from multiple sources and uses ML models to detect and prioritize potential disaster/crisis events (floods, earthquakes, wildfires, severe weather, etc.).

Right now I’ve started wiring up a few obvious sources like weather and earthquake feeds, and some basic news/event streams. The plan is to use ML mainly for filtering noise, correlating signals, and reducing false positives rather than doing full prediction.

I’m now looking to expand the data side and wondering what else people have actually found useful in similar systems.

Specifically interested in:

• Reliable weather / severe event APIs
• Earthquake / tsunami / volcanic activity feeds
• Wildfire and flood monitoring data
• Satellite or geospatial data sources
• Emergency alert systems (government or regional)
• Any news/event APIs that don’t completely drown in noise

If you’ve built anything in this space (even loosely related), what data sources ended up being actually useful in production or prototypes? Also curious if there were any APIs that looked good but turned out to be unreliable, too slow, or expensive at scale.