ORIGINAL PAPER An efficient artificial intelligence model for prediction of tropical storm surge M. Reza Hashemi 1 • Malcolm L. Spaulding 1 • Alex Shaw 1 • Hamed Farhadi 2 • Matt Lewis 3 Received: 21 May 2015 / Accepted: 28 January 2016 / Published online: 9 February 2016 Ó Springer Science+Business Media Dordrecht 2016 Abstract Process-based models have been widely used for storm surge predictions, but their high computational demand is a major drawback in some applications such as rapid forecasting. Few efforts have been made to employ previous databases of synthetic/real storms and provide more efficient surge predictions (e.g. using storm similarity of an individual storm to those in the database). Here, we develop an alternative efficient and robust artificial intelligent model, which predicts the peak storm surge using the tropical storm parameters: central pressure, radius to maximum winds, forward velocity, and storm track. The US Army Corp of Engineers, North Atlantic Comprehensive Coastal Study, has recently performed numerical simulations of 1050 synthetic tropical storms, which sta- tistically represent tropical storms, using a coupled high resolution wave–surge modeling system for the east coast of the US, from Cape Hatteras to the Canadian border. This study has provided an unprecedented dataset which can be used to train artificial intelligence models for surge prediction in those areas. While numerical simulation of a storm surge at this scale and resolution (over 6 million elements scaling from 20 m to more than 100 km) is extremely expensive, the artificial intelligence takes the advantage of the previous & M. Reza Hashemi reza_hashemi@uri.edu Malcolm L. Spaulding spaulding@egr.uri.edu Alex Shaw alex_shaw@my.uri.edu Hamed Farhadi farhadi.edu@gmail.com Matt Lewis m.j.lewis@bangor.ac.uk 1 Department of Ocean Engineering and Graduate School of Oceanography, University of Rhode Island, 215 South Ferry Road, Narragansett, RI 02882, USA 2 Department of Water Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 3 School of Ocean Sciences, Bangor University, Bangor, UK 123 Nat Hazards (2016) 82:471–491 DOI 10.1007/s11069-016-2193-4