Thunderstorm classification over Karnataka Region using CNN-LSTM Hybrid approach

Resumen

Climate modeling is confronting a transformative shift due to incorporation of deep learning (DL) techniques. These methods, which are based on data, provide novel means to enhance the precision, efficiency, and comprehensibility of climate simulations and forecasts. Thunderstorms are one of the climate change issues which impact the economy and society. In this work we introduce a hybrid approach using CNN and LSTM to classify thunderstorm and non-thunderstorms using IMD dataset. Dataset consisting of observations from 1990 to 2021 is used for the classification. Combination of CNN-LSTM is compared with conventional machine learning techniques and result shows that CNN-LSTM outperforms ML techniques. CNN-LSTM method gives accuracy of 95.82% which has the highest performance compared to other techniques.