- Burak Kılıç as Project Coordinator
- Bilge Alper as Data Engineer
- Mehmet Akif Tür as Data Engineer
- Esra Kayaalp as Data Scientist
- Şevval Aysal as Data Analyst
This GitHub repository presents a comprehensive study on the application of big data methods for earthquake visualization and correlation analysis with electric field data. The research highlights the importance of utilizing large-scale datasets and advanced analytics techniques to gain valuable insights into earthquake patterns and potential relationships with other variables. The paper discusses the challenges and methodologies involved in acquiring and integrating earthquake and electric field data, emphasizing data quality and preprocessing. It explores the use of correlation analysis and visualization techniques to uncover meaningful connections between earthquakes and electric field data. The findings demonstrate the potential of big data analytics in enhancing earthquake research and risk assessment, and emphasize the need for further exploration and integration of electric field data in seismic monitoring systems.
The application of big data methods and technologies has significantly transformed earthquake research, enabling the efficient collection, processing, analysis, visualization, and correlation of vast amounts of seismic and electric field data. This paper has highlighted the importance and advantages of utilizing specific technologies, including Apache NiFi, Apache Kafka, Apache Spark, Elasticsearch, and Kibana, in earthquake visualization and exploring the relationships between seismic events and electric field measurements.
Apache NiFi facilitates seamless acquisition, integration, and preprocessing of diverse datasets from multiple sources, ensuring comprehensive data collection for earthquake research.
Apache Kafka enables reliable and scalable transmission of data, enabling real-time or near real-time processing and analysis, thus improving the ability to respond to seismic events promptly.
Apache Spark empowers researchers to leverage distributed computing capabilities to process and analyze large-scale datasets, uncovering correlations and patterns between earthquakes and electric field data efficiently.
Elasticsearch and Kibana support the storage, retrieval, and visualization of earthquake data. These technologies provide powerful search and analytics capabilities, enabling researchers to index, store, and explore earthquake data in real-time dashboards, graphs, and maps. The visualization capabilities offered by Kibana facilitate the identification of trends, anomalies, and spatial patterns, enhancing earthquake monitoring and risk assessment.
Leveraging the potential of big data methods and technologies, researchers can unlock valuable insights into earthquake behavior, improve prediction models, and contribute to more effective earthquake-related decision-making and mitigation strategies. The comprehensive analysis of seismic events and their correlation with electric field data can enhance our understanding of earthquake mechanisms and potential precursors.
In conclusion, the adoption of big data methods and technologies in earthquake research has revolutionized the field, offering new avenues for exploration and discovery. As technology continues to evolve, it is crucial for researchers and practitioners to embrace these advancements and leverage the power of Apache NiFi, Apache Kafka, Apache Spark, Elasticsearch, and Kibana to advance earthquake research, improve monitoring systems, and ultimately contribute to the safety and resilience of communities affected by seismic events.
- code/ - Contains the code implementation of the big data analytics and visualization techniques used in the study.
- data/ - Includes sample datasets used in the research. Note that some datasets may be too large to host on GitHub.
- documentation/ - Holds supplementary documents and guides related to the research and technologies used.
- figures/ - Stores figures and visualizations generated during the study.
- LICENSE - The license governing the use and distribution of the repository.
- README.md - This file, providing an overview of the research and repository.
If you find this research and code helpful, kindly consider citing our paper:
Author Lastname(s). "Title of Paper." Journal/Conference Name, Volume(Issue), Year.
For inquiries or collaboration opportunities, please feel free to reach out to us at:
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This README.md template was generated based on the abstract and conclusion of the research paper provided by the user. It aims to provide a structured and informative overview of the GitHub repository and its content related to big data methods for earthquake research.