Real-Time Tornado Forecasting Using SLHGN

Abstract

The architecture of mHGN has been improved and become Single Layer Hierarchical Graph Neuron (SLHGN). The speed of this new architecture for recognizing multidimensional patterns is faster than the one of mHGN. It is therefore more suitable for forecasting multidimensional and complex process of tornado’s genesis in real-time. Additionally, two important issues related to data handlings of non-accurate recorded data and data handlings of complex weather data have been solved. These improvements have given significant and positive quality of SLHGN in forecasting tornado. Although the accuracy and the forecasting performance cannot be calculated properly, due to the fact that weather data is not always available, the specific characteristics of the SLHGN experiment results show very promising values. This results suggest that tornado can be forecasted at least 5 h before it occurs. People in the to-be-hit area will then have adequate time to be evacuated or to escape. The deployment of SLHGN in risky areas of tornados can then be expected as a tool for reducing damages, losses, and costs. Several improvements in weather station distribution still need to be carried out in order to improve the quality of tornado forecasting using SLHGN.