A Cloud-Native Microservice Architecture for Scalable Real-Time Geohazard Monitoring: An Assessment of Predictive Model Insufficiency Amidst Increasing Seismic Events
Keywords:
Microservice Architecture, Cloud-Native Computing, Seismic Activity, Geohazard Monitoring, Scalability Patterns, Predictive Modeling, Sea Level RiseAbstract
The growing frequency and intensity of seismic events have underscored the need for robust, scalable, and real-time geohazard monitoring systems. This study proposes a cloud-native microservice architecture designed to address the performance limitations of conventional monolithic models in seismic data acquisition, processing, and prediction. The architecture leverages containerized services, distributed data pipelines, and event-driven frameworks to ensure elasticity, resilience, and low-latency communication across geospatial sensor networks. Real-time analytics were performed using streaming platforms integrated with machine learning inference modules for anomaly detection and early warning dissemination. However, the assessment reveals predictive model insufficiency when dealing with rapidly escalating seismic activities and incomplete sensor data, highlighting the constraints of existing training datasets and static learning paradigms. Experimental evaluations on simulated and live geohazard data streams demonstrate that the proposed framework significantly improves throughput and fault tolerance while maintaining near-real-time responsiveness. The findings emphasize the critical need for adaptive and self-learning predictive models within cloud-native architectures to enhance future seismic hazard forecasting accuracy and operational scalability.
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Copyright (c) 2025 Dr. Elias R. Vance, Prof. Coraline Q. Harthwick (Author)
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