AI-Powered Earthquake Monitoring and Risk Analysis System: Real-Time Early Warning and Intelligent Decision-Making

The AI-powered earthquake monitoring and risk analysis system introduced in this article integrates USGS real-time earthquake data, Streamlit interactive interface, and Power BI visual analysis. It has functions such as real-time monitoring, AI risk prediction, and tsunami tracking. By combining data science with emergency management, the project builds a practical disaster early warning tool that serves multiple scenarios including emergency management, scientific research, and public education.

Earthquakes are highly destructive natural disasters that cause massive casualties and economic losses every year. Timely and accurate monitoring and early warning are crucial. Traditional systems rely on seismograph networks and can provide high-precision seismic source parameters, but they have shortcomings in data visualization, risk analysis, and public early warning. Big data and AI technologies have promoted the emergence of a new generation of intelligent systems, which can realize real-time monitoring, risk prediction, historical trend analysis, and decision support.

The project adopts a modular design. Core components include a real-time monitoring center (Plotly interactive map, event stream), AI risk prediction (multi-dimensional scoring, radar chart, waveform analysis), and tsunami tracker (ocean earthquake screening, potential assessment, wave simulation), etc. In terms of the technology stack, the data source is the USGS earthquake database (real-time + historical data). The front end uses Streamlit to quickly build interactive applications, visualization relies on Plotly, and AI functions include risk prediction models, anomaly detection, cluster analysis, etc.

Emergency management departments can use the system to achieve situational awareness, risk assessment, and decision support; scientific research institutions can easily access and analyze data and verify theories; in public education, it can popularize earthquake knowledge and enhance risk awareness; the insurance and financial industry uses it for risk pricing, loss estimation, and portfolio evaluation.

System innovations include multi-dimensional risk modeling (integrating factors such as physical parameters and population distribution), combination of real-time and historical data (balancing real-time monitoring and long-term trend analysis), balance between professionalism and accessibility (meeting the needs of both professional and ordinary users), and scalable architecture (facilitating subsequent function expansion).

Current limitations: The AI model is for risk assessment rather than earthquake prediction; data dependence on USGS may lead to delays or omissions; model accuracy is limited by training data. Future improvements: Integrate multi-source data, apply deep learning, develop mobile terminals, and establish community collaboration.

This system demonstrates the application value of data science in disaster management. By integrating public data sources, AI, and Web technologies, it builds a feature-rich monitoring platform. Although earthquake prediction remains a difficult problem, the system can help with rapid post-earthquake response, scientific risk assessment, and effective rescue organization to reduce disaster losses.