Spatio-Temporal Analysis of Hybrid CNN-GRU Model for Prediction of Earthquake for Disaster Management
Keywords:
Earthquake, Prediction, ; Convolutional Neural Network, Gated Recurrent Unit, Seismic dataAbstract
Earthquake prediction holds immense significance for disaster management and public safety. This study presents a novel approach for earthquake prediction through spatio-temporal analysis using a Hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model. The methodology integrates the strengths of CNNs in spatial feature extraction and GRUs in temporal pattern recognition, offering a comprehensive understanding of seismic events. The research incorporates seismic data enriched with geographical parameters, facilitating the analysis of earthquake occurrences across diverse regions. The model's spatial component, CNN, excels in capturing intricate spatial features within seismic data. In parallel, the temporal component, GRU, effectively discerns evolving patterns of seismic activity over time. This hybrid architecture ensures a holistic analysis of seismic data, enabling early detection and accurate earthquake prediction. To evaluate the model's efficacy, extensive experiments are conducted using seismic data from various regions. Performance metrics such as mean absolute error, mean squared error, and root-mean-square error are employed to assess predictive accuracy. Comparative analysis demonstrates the superiority of the Hybrid CNN-GRU model in earthquake prediction, particularly for large seismic events. The proposed methodology offers valuable insights for enhancing earthquake prediction systems, contributing to disaster management strategies and bolstering public safety measures. This research represents a significant advancement in the field of seismology, providing a robust framework for mitigating the impact of earthquakes on communities worldwide. In our seismic prediction study, we achieved remarkable results with our hybrid CNN-GRU model, attaining a high accuracy rate of 98.67%. The proposed model exhibited a significantly low loss, indicating its proficiency in capturing intricate spatial-temporal patterns within seismic data. These findings underscore the model's potential for enhancing earthquake forecasting accuracy, making it a valuable contribution to early warning systems and seismic research.
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