A Database Engineered System for Big Data Analytics on Tornado Climatology
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2024
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| author | Bian, Fengfan Leung, Carson K. Grenier, Piers Pu, Harry Ning, Samuel Cuzzocrea, Alfredo |
| author_facet | Bian, Fengfan Leung, Carson K. Grenier, Piers Pu, Harry Ning, Samuel Cuzzocrea, Alfredo |
| contents | Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system aids in predicting tornado occurrences by identifying the data points that form the basis of a tornado warning. Evaluation on US data highlights the advantages of using a classification forecasting recurrent neural network (RNN) model. The results highlight the effectiveness of our database engineered system for big data analytics on tornado climatology-especially, in accurately predict-ing tornado lead-time, magnitude, and location, contributing to the development of sustainable cities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17668 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Database Engineered System for Big Data Analytics on Tornado Climatology Bian, Fengfan Leung, Carson K. Grenier, Piers Pu, Harry Ning, Samuel Cuzzocrea, Alfredo Databases Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system aids in predicting tornado occurrences by identifying the data points that form the basis of a tornado warning. Evaluation on US data highlights the advantages of using a classification forecasting recurrent neural network (RNN) model. The results highlight the effectiveness of our database engineered system for big data analytics on tornado climatology-especially, in accurately predict-ing tornado lead-time, magnitude, and location, contributing to the development of sustainable cities. |
| title | A Database Engineered System for Big Data Analytics on Tornado Climatology |
| topic | Databases |
| url | https://arxiv.org/abs/2409.17668 |