Enhanced Cyclone Intensity Estimation Using Deep Learning Models

Enhanced Cyclone Intensity Estimation Using Deep Learning Models

1          G. Surekha,   2 Dr Md Zaheer Ahmed,  3  Dr A.Swarna, 4 Dr Y.Praveen Kumar

1 2 3 4 Department of Computer Science and Engineering, Vidya Jyothi Institute of Technology, Hyderabad, India

1 surekha@vjit.ac.in ,         2 zaheerahmed@vjit.ac.in .      3swarnaa@vjit.ac.in ,     4 praveenkumar@vjit.ac.in

ABSTRACT

Every year in India, tropical cyclones pose one of the greatest threats to life and property. They include a variety of hazards, each of which can have a significant impact on life and property, such as storm surge, flooding, extreme winds, tornadoes, and lighting. These hazards interact with one another, substantially increasing the risk of loss of life and material damage. According to a study on extreme weather events, as many as 117 cyclones hit India in 50 years from 1970 to 2019, claiming over 40,000 lives. Due to advancements in technology, detection of cyclones has become much easier. Cyclones are categorized into various categories based on their intensity which is calculated by various methods and sources. Estimation of the intensity of cyclone helps them to categorize. In this paper we are trying to estimate the intensity of cyclones by using the state-of-the-art deep learning models as they are pre-trained on large data sets.

Keywords: Tropical cyclones, Hazards, Intensity estimation, Deep learning models, Detection, Data sets, Cyclone intensity, Weather prediction, Disaster management.