Thunderstorm classification over Karnataka Region using CNN-LSTM Hybrid approach

Madhusudhan HS; Ajay Kumar; Punit Gupta

doi:10.5935/jetia.v12i61.4094

Authors

Madhusudhan HS Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia , Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
Ajay Kumar Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia , School of Computer Science & Engineering, IILM University, Greater Noida, Delhi NCR, India
Punit Gupta National college of Ireland, Dublin , Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

DOI:

https://doi.org/10.5935/jetia.v12i61.4094

Abstract

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.