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Global physical event detection has traditionally relied on dense coverage ofphysical sensors around the world; while this is an expensive undertaking,there have not been alternatives until recently. The ubiquity of socialnetworks and human sensors in the field provides a tremendous amount ofreal-time, live data about true physical events from around the world. However,while such human sensor data have been exploited for retrospective large-scaleevent detection, such as hurricanes or earthquakes, they has been limited to nosuccess in exploiting this rich resource for general physical event detection.  Prior implementation approaches have suffered from the concept driftphenomenon, where real-world data exhibits constant, unknown, unbounded changesin its data distribution, making static machine learning models ineffective inthe long term. We propose and implement an end-to-end collaborative driftadaptive system that integrates corroborative and probabilistic sources todeliver real-time predictions. Furthermore, out system is adaptive to conceptdrift and performs automated continuous learning to maintain high performance.We demonstrate our approach in a real-time demo available online for landslidedisaster detection, with extensibility to other real-world physical events suchas flooding, wildfires, hurricanes, and earthquakes.

Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources