Artificial neural network-based storm surge forecast model: Practical application to Sakai Minato, Japan

Kim, Sooyoul, Pan, Shunqi ORCID: https://orcid.org/0000-0001-8252-5991 and Mase, Hajime 2019. Artificial neural network-based storm surge forecast model: Practical application to Sakai Minato, Japan. Applied Ocean Research 91 , 101871. 10.1016/j.apor.2019.101871

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Abstract

The present study describes a novel way of a systematic and objective selection procedure for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN-SFM) with the 5, 12 and 24 h-lead times and its application to Sakai Minato area on the Tottori coast, Japan. The selection procedure guides how to determine the superiority of the best performing model in terms of the appropriate combination of unit number in the hidden layer and parameter in the input layer. In the application of ANN-SFM to Sakai Minato, it is found that the best 5 and 12 h-forecast ANN-SFMs are established with the most suitable set of 70 units (the number of hidden neurons) and the input components of surge level, sea level pressure, the depression rate of sea level pressure, longitude, latitude, central atmospheric pressure and highest wind speed. The best 24 h-forecast ANN-SFM is determined with 160 units and the input parameters of surge level, sea level pressure, the depression rate of sea level pressure, longitude and latitude. The proposed method of the selection procedure is able to be adaptable to other coastal locations for the development of the artificial neural network-based storm surge forecast model as establishing the superiority of the most relevant set combining unit numbers and input parameters.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Engineering
Publisher:	Elsevier
ISSN:	0141-1187
Date of First Compliant Deposit:	3 October 2019
Date of Acceptance:	8 July 2019
Last Modified:	28 Nov 2024 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/124790

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