Hybrid CNN-LSTM Approach for Geolocation-based Earthquake Risk Prediction using USGS Data

Abstract

Predicting earthquake risk accurately is essential for reducing the devastating effects of seismic activity. To improve earthquake risk prediction by geolocation, this study presents a hybrid learning strategy that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. The research uses CNNs to extract spatial characteristics from geophysical and seismic data and LSTM networks to capture temporal dependencies in the sequence of events using the United States Geological Survey (USGS) dataset. The model creates a strong foundation for estimating earthquake risk at the local and regional levels by combining these complementary approaches. By integrating these complementary approaches and utilizing geolocation data such as latitude, longitude, depth, and proximity to fault lines, the model provides a robust framework for local and regional earthquake risk estimation, providing granular insights into vulnerable areas. According to experimental data, the hybrid CNN-LSTM model works better than conventional machine learning techniques, resulting in reduced false positives and increased prediction accuracy. Additionally, the model demonstrates flexibility and scalability, enabling real-time updates through the use of streaming data from IoT-enabled sensors and seismometers.