Prediction of Lightning in South Africa using an LSTM Neural Network Model using Historical Lightning Data

Researcher: Yaseen Essa , University of the Witwatersrand, Johannesburg
Supervisors: Dr Ritesh Ajoodah and Dr Hugh Hunt , University of the Witwatersrand, Johannesburg

We evaluated the prediction ability of the Long-Short Term-Memory-Recurrent Neural Network (LSTM) model to predict short-term lightening flash densities within South Africa using historical lightening events. We predicted the lightening flash densities for one-hour periods for two areas within South Africa using data from the South African Lightening Detection Network. Models were trained using four years of data and predictions were made for every one-hour interval for one year. The models were tested repeatedly and cross-validated. We found a combined Mean Absolute Error of 2.87 lightening-flashes.hour and a combined Mean Squared Error of 1209. The model predicted 3 in 10 lightening events. We believe that LSTM models are useful tools to manage lightening risk.