{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-hurricane-trajectories-using-a","title":"Predicting Hurricane Trajectories using a Recurrent Neural Network","arxiv_id":"1802.02548","date":"2018-02-01","proceeding":null,"authors":["Sheila Alemany","Jonathan Beltran","Adrian Perez","Sam Ganzfried"],"abstract":"Hurricanes are cyclones circulating about a defined center whose closed wind\nspeeds exceed 75 mph originating over tropical and subtropical waters. At\nlandfall, hurricanes can result in severe disasters. The accuracy of predicting\ntheir trajectory paths is critical to reduce economic loss and save human\nlives. Given the complexity and nonlinearity of weather data, a recurrent\nneural network (RNN) could be beneficial in modeling hurricane behavior. We\npropose the application of a fully connected RNN to predict the trajectory of\nhurricanes. We employed the RNN over a fine grid to reduce typical truncation\nerrors. We utilized their latitude, longitude, wind speed, and pressure\npublicly provided by the National Hurricane Center (NHC) to predict the\ntrajectory of a hurricane at 6-hour intervals. Results show that this proposed\ntechnique is competitive to methods currently employed by the NHC and can\npredict up to approximately 120 hours of hurricane path.","url_abs":"http://arxiv.org/abs/1802.02548v3","url_pdf":"http://arxiv.org/pdf/1802.02548v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-hurricane-trajectories-using-a","repo_url":"https://github.com/stormalytics/hurricane-frocasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.02548","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}