Disaster management and mitigation in coastal regions and tectonic fault zones require a fundamental shift from manual, expert-committee-dependent response models to closed-loop automated architectures. By combining the Tensor Stress Index function (TSI_{t+1}), Edge AI processing, distributed community observation networks, security systems, and reconnaissance drones (UAVs) deployed from subsea stations, the system establishes a high-speed defense layer that optimizes both cost-efficiency and operational reliability. This paper presents an integrated system architecture featuring: The TSI_{t+1} Function: A multidimensional variable space that aggregates tectonic displacement velocity, subsea pressure, coastal water level, microseisms, and shelf tilt, modulated by spatial risk multipliers (R_{risk}) to automatically trigger emergency states. Micro-Sandbox Field Tier: Flexible utilization of fixed community cameras, civilian devices, and subsea station UAVs to establish continuous 24/7 baseline observation and targeted high-resolution reconnaissance. Closed-Loop Automated Response: Automated anomaly detection, low-latency live video verification, tactical navigation, and federated learning integration for continuous accuracy refinement. LERC Financial Framework Integration: Linking verified edge data and smart contracts with Local Ecological Recovery Credit (LERC) mechanisms for transparent, automated emergency resource and capital disbursement without bureaucratic delays. Through comprehensive operational scenarios and parameter evolution models, this architecture demonstrates a paradigm shift toward real-time, autonomous, and financially sustainable coastal disaster governance.
Minh Trí Phạm· Zenodo (CERN European Organi...· 0 citations
Disaster management and mitigation in coastal regions and tectonic fault zones require a fundamental shift from manual, expert-committee-dependent response models to closed-loop automated architectures. By combining the Tensor Stress Index function (TSI_{t+1}), Edge AI processing, distributed community observation networks, security systems, and reconnaissance drones (UAVs) deployed from subsea stations, the system establishes a high-speed defense layer that optimizes both cost-efficiency and operational reliability. This paper presents an integrated system architecture featuring: The TSI_{t+1} Function: A multidimensional variable space that aggregates tectonic displacement velocity, subsea pressure, coastal water level, microseisms, and shelf tilt, modulated by spatial risk multipliers (R_{risk}) to automatically trigger emergency states. Micro-Sandbox Field Tier: Flexible utilization of fixed community cameras, civilian devices, and subsea station UAVs to establish continuous 24/7 baseline observation and targeted high-resolution reconnaissance. Closed-Loop Automated Response: Automated anomaly detection, low-latency live video verification, tactical navigation, and federated learning integration for continuous accuracy refinement. LERC Financial Framework Integration: Linking verified edge data and smart contracts with Local Ecological Recovery Credit (LERC) mechanisms for transparent, automated emergency resource and capital disbursement without bureaucratic delays. Through comprehensive operational scenarios and parameter evolution models, this architecture demonstrates a paradigm shift toward real-time, autonomous, and financially sustainable coastal disaster governance.
Minh Trí Phạm· Zenodo (CERN European Organi...· 0 citations
Active volcanic regions require a fundamental shift from manual, expert-committee-dependent response models toward closed-loop automated architectures. This study proposes an integrated early warning framework that combines the Tensor Stress Index (TSI_{t+1}) function, edge computing (Edge AI), and distributed community-based observation networks. By utilizing consumer and fixed surveillance cameras as edge nodes within a micro-sandbox tier, the system achieves continuous 24/7 baseline observation at a fraction of traditional infrastructure costs. When multivariate geological indicators—including magma reservoir pressure (P), surface deformation (\epsilon), micro-seismic frequency (S), and gas emission rates (G)—drive the TSI_{t+1} function beyond safety tolerance thresholds, the architecture autonomously flags anomalies, triggers real-time visual verification via low-latency mobile interfaces, and coordinates rapid evacuation routes. Furthermore, the framework anchors operational management into the Local Ecological Restoration Credit (LERC) economic structure, leveraging formally verified smart contracts for transparent resource allocation and automated financial disbursement without administrative bottlenecks.
Minh Trí Phạm· Zenodo (CERN European Organi...· 0 citations
Active volcanic regions require a fundamental shift from manual, expert-committee-dependent response models toward closed-loop automated architectures. This study proposes an integrated early warning framework that combines the Tensor Stress Index (TSI_{t+1}) function, edge computing (Edge AI), and distributed community-based observation networks. By utilizing consumer and fixed surveillance cameras as edge nodes within a micro-sandbox tier, the system achieves continuous 24/7 baseline observation at a fraction of traditional infrastructure costs. When multivariate geological indicators—including magma reservoir pressure (P), surface deformation (\epsilon), micro-seismic frequency (S), and gas emission rates (G)—drive the TSI_{t+1} function beyond safety tolerance thresholds, the architecture autonomously flags anomalies, triggers real-time visual verification via low-latency mobile interfaces, and coordinates rapid evacuation routes. Furthermore, the framework anchors operational management into the Local Ecological Restoration Credit (LERC) economic structure, leveraging formally verified smart contracts for transparent resource allocation and automated financial disbursement without administrative bottlenecks.
Minh Trí Phạm· Zenodo (CERN European Organi...· 0 citations
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