A quantum-enhanced deep learning architecture with adaptive resonance circuits for high-precision tsunami prediction
Naga Santha Kumari Cheeti, Venugopal Boppana, Suneetha Davuluri +1 more
Natural disasters like tsunamis pose significant threats to coastal communities worldwide, necessitating advanced prediction systems for effective early warning. This study introduces the Quantum-Enhanced Deep Learning for Tsunami Prediction (QEDLTP) architecture, a novel approach combining quantum computing principles with deep learning techniques. Utilizing an extensive dataset spanning 1995-2023, comprising 18,742 seismic events and bathymetric measurements, our framework implements a dual-stream processing architecture with Adaptive Quantum Resonance Circuits (AQRC). The model employs an innovative Quantum Feature Selection (QFS) mechanism that dynamically adjusts quantum entanglement patterns based on input characteristics. The Proposed approach processes seismic waveforms from 2,000 global stations and high-resolution bathymetric data simultaneously, achieving 98.7% prediction accuracy with response times under 3 seconds. The architecture demonstrates superior performance in complex geological settings, reducing false positives by 76% compared to traditional methods. The QEDLTP framework’s quantum-enhanced processing enables 78% feature dimensionality reduction while maintaining 96.5% information retention, significantly improving computational efficiency. Testing across 250 historical tsunami events validates the model’s robustness and reliability. This research establishes new benchmarks in tsunami prediction systems and opens avenues for quantum computing applications in geophysical analysis and natural disaster prediction.
CRC Press eBooks•Jan 29, 2026•0 citations