Myay Gyi AI: A Machine Learning-Based Real-Time Earthquake Monitoring and Aftershock Prediction Platform

Myay Gyi AI is an open-source intelligent earthquake monitoring platform for Myanmar and the world. It integrates USGS real-time data and machine learning technologies to provide real-time earthquake monitoring, trend analysis, and aftershock probability prediction functions. It aims to enhance earthquake disaster response capabilities and was built by Htut Myat Oo based on the Streamlit framework.

Earthquakes are highly destructive natural disasters, and monitoring and early warning are crucial. Myay Gyi AI (meaning "Earth Intelligence" in Burmese) was born out of the need to improve Myanmar's earthquake response capabilities. Traditional systems focus on post-event analysis; this project innovatively introduces machine learning to process real-time data, integrates USGS global real-time earthquake data, and realizes real-time display and aftershock probability estimation.

The platform uses a Python+Streamlit tech stack. Core functions include: 1. Real-time earthquake monitoring dashboard (obtains data via USGS API, displays via heatmaps and time series, supports multi-dimensional filtering by magnitude, depth, geographic location, etc.); 2. Trend exploration and analysis tool (uses statistical visualization to identify periodic patterns and abnormal signals in seismic activity); 3. Aftershock probability prediction engine (trains models based on historical data to estimate the occurrence probability and time distribution of aftershocks after a main earthquake).

Aftershock prediction relies on modeling historical earthquake sequences, with training data from the USGS historical earthquake catalog. Features cover dimensions such as main earthquake magnitude, depth, geographic location, fault type, and regional geological stress state; algorithms selected are ensemble learning methods suitable for time series (e.g., random forest or gradient boosting trees); data processing uses a real-time streaming architecture, updating data and triggering model recalculation within minutes.

1. General public: Intuitively understand global earthquake dynamics, enhance risk awareness, and explore earthquake history of concerned regions; 2. Researchers: Convenient data exploration tool supporting trend analysis and pattern recognition; open-source nature allows function expansion or algorithm improvement; 3. Emergency management departments: Aftershock probability references assist in risk assessment and emergency resource allocation.

The tech stack includes ObsPy (seismic data processing), scikit-learn/TensorFlow (machine learning); real-time data is obtained via USGS API, with automatic caching and incremental updates implemented; visualization uses Plotly (statistical charts) and Folium (geographic distribution); Streamlit supports one-click deployment of web applications (local or cloud).

Limitations: Earthquake prediction is a scientific challenge; aftershock estimation is based on historical statistical laws, and accuracy is limited by data representativeness and model assumptions. Future directions: Introduce fine geological structure data, InSAR satellite observation data, develop mobile applications, and establish crowdsourced data collection mechanisms. This open-source project contributes to the advancement of disaster early warning technology.