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Research Paper | Computer Science and Information Technology | United States of America | Volume 14 Issue 5, May 2025 | Popularity: 3.4 / 10
ETL and BI in Military-Civilian Collaboration for Disaster Preparedness
Shiva Kumar Vuppala
Abstract: In the face of increasing climate-related catastrophes and large-scale emergencies, the need for seamless coordination between military and civilian agencies has become more critical than ever. Effective disaster preparedness relies heavily on the ability to integrate, analyze, and act on diverse data sources. However, traditional systems often suffer from fragmented data silos, inconsistent formats, and slow decision-making processes, hindering timely and coordinated responses. This paper explores the application of Extract, Transform, Load (ETL) processes and Business Intelligence (BI) tools as a robust framework to bridge this operational gap. ETL pipelines are used to collect and standardize data from weather agencies, emergency services, defense systems, and non-governmental organizations (NGOs), transforming it into a structured format suitable for analysis. Once integrated, BI platforms generate real-time dashboards, predictive models, and visual reports that enhance situational awareness and facilitate proactive decision-making across agencies. The proposed method also includes the implementation of an MLP-LSTM architecture for forecasting critical disaster variables, such as casualty rates and resource needs, based on historical and real-time data. By combining temporal sequence learning with complex feature extraction, the model significantly improves the accuracy and speed of disaster impact predictions. Real-world scenarios, including responses to hurricanes and wildfires, are used to validate the effectiveness of this approach. Graphs illustrating improvements in data quality, loading time, response efficiency, and reduced casualties further reinforce the benefits of this system. Despite the advancements, limitations remain in terms of interoperability, data privacy, and the need for real-time automation in some legacy systems. Future work will focus on enhancing AI-driven ETL processes, incorporating IoT-based real-time feeds, and establishing standardized data-sharing protocols across jurisdictions. Overall, this study presents an integrated, data-driven model that strengthens disaster readiness and response through improved collaboration between military and civilian infrastructures.
Keywords: Disaster Preparedness, ETL, Business Intelligence, Military-Civilian Collaboration, MLP-LSTM, Real-Time Analytics
Edition: Volume 14 Issue 5, May 2025
Pages: 639 - 649
DOI: https://www.doi.org/10.21275/SR25509004027
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