Automated response system for earthquake using deep learning techniques
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Date
2025
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Volume Title
Publisher
NHCE
Abstract
Earthquakes are one of the most destructive natural disasters, capable of causing significant loss of life, damage to infrastructure, and long-term economic repercussions. The unpredictable nature of earthquakes makes them a critical concern for disaster management, as traditional methods of detection and response often fail to provide timely warnings. Current earthquake monitoring systems depend on seismic networks, which detect the occurrence of tremors after they have happened, providing little opportunity for preventative measures. This study proposes the development of an Automated Response System for Earthquakes Using Deep Learning Techniques, with an emphasis on using advanced machine learning methods such as Long Short-Term Memory (LSTM) networks to address this problem.
The primary objective of this system is to detect seismic events in real-time and to predict their impact, enabling a quick response that can save lives and mitigate damage. Unlike traditional earthquake monitoring techniques, which primarily rely on detecting seismic waves after the event has occurred, deep learning models like LSTM are capable of analyzing seismic data in real-time to predict earthquakes with a degree of accuracy and lead time that was previously not possible. LSTM, a type of recurrent neural network (RNN), is especially well-suited for time-series forecasting tasks, such as predicting earthquake events, due to its ability to learn from long-term dependencies in sequential data. By analyzing the continuous stream of seismic data, LSTM models can be trained to recognize patterns that indicate imminent earthquakes, allowing for early detection.