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

TL;DR

This paper presents an adaptive system that combines corroborative and probabilistic data sources for real-time physical event detection, effectively handling concept drift and demonstrating success in landslide detection with potential for other disasters.

Contribution

It introduces a novel end-to-end collaborative system that adapts to concept drift and integrates diverse data sources for real-time physical event detection.

Findings

Effective real-time landslide detection demonstrated

System maintains high performance despite data distribution changes

Extensible framework applicable to various physical events

Abstract

Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective large-scale event detection, such as hurricanes or earthquakes, they has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where real-world data exhibits constant, unknown, unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an end-to-end collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver real-time predictions. Furthermore, out system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a real-time demo available online for landslide disaster detection, with extensibility to other real-world physical events such as flooding, wildfires, hurricanes, and earthquakes.