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

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

Kim, Sooyoul / Pan, Shunqi / Mase, Hajime

Highlights • The selection procedure for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN-SFM) is described. • The selection procedure is applied to the Sakai Minato on the Tottori coast, Japan. • The best performing model is established with the pair of the suitable parameter set and corresponding unit number in the input and hidden layers.

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.