Title: Earthquake prediction using deep learning based-recurrent neural network technique

Authors: J. Sahaya Ruben; M. Adams Joe; M. Anand; M. Prem Anand

Addresses: Department of Civil Engineering, Rohini College of Engineering and Technology, Anjugramam 629401, Tamil Nadu, India ' Department of Civil and Mechanical Engineering, Middle East College, Muscat, Sultanate of Oman ' Department of Civil Engineering, Annamalai Polytechnic College, Chettinad 630102, Tamil Nadu, India ' Department of Civil Engineering, VSVN Polytechnic College, Virudhunagar 626001, Tamil Nadu, India

Abstract: This article explores the application of deep learning techniques to enhance earthquake prediction and detection, addressing the critical need for improved disaster preparedness and risk mitigation. With natural disasters like earthquakes causing widespread harm to people, and ecosystems, the study focuses on leveraging machine learning methodologies, including recurrent neural networks and reinforcement learning, to develop an innovative earthquake prediction framework. The proposed approach begins with feature extraction using CNN to capture essential seismic data patterns. Subsequently, the RNN model is trained to analyse time series seismic data, allowing for the prediction of earthquake events with enhanced precision. In addition, Q-learning is integrated into the process to make informed decisions based on the current state, potentially reducing false alarms and improving overall prediction accuracy. The promising results and potential impact of this research underscore the importance of ongoing efforts to harness technology for more effective earthquake prediction and mitigation strategies.

Keywords: convolutional neural network; CNN; recurrent neural network; RNN; Q-learning; deep learning.

DOI: 10.1504/IJESMS.2025.149574

International Journal of Engineering Systems Modelling and Simulation, 2025 Vol.16 No.6, pp.387 - 397

Received: 09 Nov 2023
Accepted: 18 Feb 2025
Published online: 07 Nov 2025 *

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