Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

Authors

  • Sandeep Kumar Shaheed Bhagat Singh College, University of Delhi, Delhi, India Indraprastha Institute of Information Technology, New Delhi, India
  • Koushik Biswas Indraprastha Institute of Information Technology, New Delhi, India
  • Ashish Kumar Pandey Indraprastha Institute of Information Technology, New Delhi, India

DOI:

https://doi.org/10.1609/aaai.v35i17.17741

Keywords:

Natural Sciences, Environmental Sustainability

Abstract

The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.

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Published

2021-05-18

How to Cite

Kumar, S., Biswas, K., & Pandey, A. K. (2021). Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14831-14839. https://doi.org/10.1609/aaai.v35i17.17741

Issue

Section

AAAI Special Track on AI for Social Impact