  Tornadoes are among the most intense atmospheric vortex phenomena and pose
significant challenges for detection and forecasting. Conventional methods,
which heavily depend on ground-based observations and radar data, are limited
by issues such as decreased accuracy over greater distances and a high rate of
false positives. To address these challenges, this study utilizes the Seamless
Hybrid Scan Reflectivity (SHSR) dataset from the Multi-Radar Multi-Sensor
(MRMS) system, which integrates data from multiple radar sources to enhance
accuracy. A novel hybrid model, the Kalman-Convolutional BiLSTM with Multi-Head
Attention, is introduced to improve dynamic state estimation and capture both
spatial and temporal dependencies within the data. This model demonstrates
superior performance in precision, recall, F1-Score, and accuracy compared to
methods such as K-Nearest Neighbors (KNN) and LightGBM. The results highlight
the considerable potential of advanced machine learning techniques to improve
tornado prediction and reduce false alarm rates. Future research will focus on
expanding datasets, exploring innovative model architectures, and incorporating
large language models (LLMs) to provide deeper insights. This research
introduces a novel model for tornado prediction, offering a robust framework
for enhancing forecasting accuracy and public safety.
