AI-Powered LSTM-Based Earthquake Forecasting and Alert System

Abstract

The AI-Powered LSTM-Based Earthquake Forecasting and Alert System is an intelligent software application developed to forecast potential earthquake events using historical seismic data. This project employs machine learning techniques, specifically a Long Short-Term Memory (LSTM) neural network, to analyze time-series data of past earthquakes and predict future occurrences based on learned patterns. The system is built with a robust backend that handles data collection, preprocessing, and model execution. Key functionalities include automatic data cleaning, exploratory data analysis (EDA), generation of informative visualizations like yearly frequency graphs and heatmaps, and a prediction interface. It utilizes historical earthquake data sourced from public datasets and processes it to ensure accuracy and relevance for training. The trained LSTM model is capable of identifying temporal dependencies within the data, enabling more informed predictions about seismic trends.

Though this system is not intended to replace geological sensors or official early warning systems, it demonstrates the potential of artificial intelligence and data science in supporting disaster preparedness strategies. The system can serve as a supplementary tool for researchers, government agencies, and emergency responders seeking to gain insights into seismic behavior over time.