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Data Reporting and Recording

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RTEDAP framework for real-time event-driven data aggregation and processing in tsunami early warning systems.

M Umadevi1, Dhanalakshmi Gopal2, V S Nishok3

  • 1Department of Electronics and Communication Engineering, INFO Institute of Engineering, Coimbatore, India. umadeviselvam2001@gmail.com.

Scientific Reports
|June 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces RT-EDAP, a novel framework for tsunami early warning systems. It significantly improves real-time data processing and alert dissemination, enhancing disaster management capabilities.

Keywords:
Data aggregationEvent-driven architectureReal-time data processingStream processing frameworksTsunami early warning system

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Area of Science:

  • Earth and Environmental Sciences
  • Computer Science and Engineering

Background:

  • Existing tsunami early warning systems (TEWS) struggle with latency, scalability, and real-time data processing.
  • These limitations hinder effective tsunami prediction and timely alert dissemination to coastal communities.

Purpose of the Study:

  • To develop a novel framework, RT-EDAP (Real-Time Event-Driven Data Aggregation and Processing), for a robust, low-latency, and scalable TEWS.
  • To enhance the efficiency of tsunami prediction and alert dissemination by addressing current system challenges.

Main Methods:

  • Utilized Edge Computing and Stream Processing Frameworks (Apache Kafka, Apache Flink) for local data processing at edge nodes.
  • Implemented an event-driven architecture prioritizing critical seismic anomalies and integrated multi-source data (seismic sensors, tide gauges, GPS).
  • Employed lightweight edge models and centralized machine learning (Temporal Convolutional Networks - TCNs) for improved event classification accuracy.

Main Results:

  • RT-EDAP demonstrated high prediction accuracy (95%) and low processing latency (50-60ms).
  • The framework effectively handles multi-source data streams and scales to manage high event rates.
  • Outperformed traditional methods in performance metrics including latency, throughput, and scalability.

Conclusions:

  • RT-EDAP offers a scalable, fault-tolerant solution for enhancing tsunami early warning systems.
  • The framework significantly improves real-time data processing and response times, advancing disaster management.
  • RT-EDAP represents a substantial advancement in developing more effective and responsive TEWS.