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.
@article{arxiv.2409.17668,
title = {A Database Engineered System for Big Data Analytics on Tornado Climatology},
author = {Fengfan Bian and Carson K. Leung and Piers Grenier and Harry Pu and Samuel Ning and Alfredo Cuzzocrea},
journal= {arXiv preprint arXiv:2409.17668},
year = {2024}
}