A biologically based machine learning approach to tropical cyclone intensity forecasting
(English)Manuscript (preprint) (Other academic)
A biologically based ANN using four hierarchical levels, is trained and tested using temporal sequences of 2D inputs to forecast Tropical Cyclone (TC) intensity12, and 24 hours ahead in the Atlantic basin. We use five parallel input layers to feed infrared, ocean heat content, sea-level pressure, wind direction and wind speed images into the network. Forecasts are produced in the Saffir-Simpson hurricane intensity scale and are compared to the observed wind speeds in the TC best track data on two separate test datasets for validation. Forecasting accuracy is more than 95% for the test dataset containing temporal continuations of the TC lifecycle time-step images that are excluded from training, whereas, forecasting accuracy is between 30% and 55%, when images of a novel TC are used for testing. This result reveals that biologically inspired ANNs have a potential to be further developed into an effective TC intensity forecasting technique.
Biologically based artificial neural networks; bi-directionally connected networks; Markov chain; temporal sequence learning; simple recurrent networks
Other Computer and Information Science
IdentifiersURN: urn:nbn:se:liu:diva-123196OAI: oai:DiVA.org:liu-123196DiVA: diva2:877262