Application of recurrent neural network for prediction of the time-varying storm surge. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Igarashi, Yusuke; Tajima, Yoshimitsu
• Is Part Of: Coastal engineering journal. Volume 63:Issue 1(2021)
• Page Start: 68
• Page End: 82
• Publication Date: 2021-01-02
• Subjects: Storm surge forecast -- typhoon prediction -- deep learning -- recurrent neural network -- long-short term memory; Coastal engineering -- Periodicals; Coasts -- Periodicals; Hydraulic engineering -- Periodicals; Coast changes -- Periodicals; Shore protection -- Periodicals; Coast changes; Coastal engineering; Coasts; Hydraulic engineering; Shore protection; Periodicals; 627.58
• DOI: 10.1080/21664250.2020.1868736
• Languages: English
• ISSNs: 2166-4250

Abstract

ABSTRACT: This study investigates the overall performance of non-linear regression models based either on DNN or on RNN for the fast predictions of time-varying storm surge heights. We selected Tokyo-Bay as a case study site and conducted numerical simulations of storm surges induced by 151 recorded typhoons that passed around Japan from 1951 to 2017. Obtained time-series data of the storm surge heights at selected 10 target points were used as outcome variables of the non-linear regression model. The corresponding predictor variables are obtained from the time series of typhoon data and the recent time history of the storm surges height at the same target point. This study then performed a parametric study to explore the optimum model setups of: (i) types of optimizer; (ii) the number of hidden layers (iii) the number of nodes of each hidden layer; and (iv) the duration time and the lead time of the time-series data of the typhoon and the recent storm surge height. Besides the optimum conditions of the present regression model, it was found that RNN showed clearly better predictive skills than DNN, and the recent history of the storm surge did not significantly improve the predictive performance of the present regression model.