Accuracy Verification of Coastal Wave Numerical Forecast Model, GPV (Cwm), for Marine Construction

Hara, Chisato; Kim, Sooyoul; Kurahara, Yoshinosuke; Nishiyama, Yamato; Takeda, Masahide; Mase, Hajime

doi:10.2208/jscejoe.75.i_935

Cited by 6 publications

(1 citation statement)

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“…For verifying the trained modified Xception model, the resulting forecasted wave height and period from the one year of test data were compared with the corresponding wave height and period of NOWPHAS observations at each location individually. Hara et al 9) , introduced a threshold for a hit rate by using a 1 m wave height and 10 s wave period to judge commencement of nearshore marine const-ruction. For verification, the wave height threshold was set to 1 m. Fig.…”

Section: Verification Methodsmentioning

confidence: 99%

One Year Verification of Wave Predictions by Deep Learning Using Xception

Tom

Ikemoto

Mase

et al. 2020

Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal E

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Accurate and timely wave forecast is vital for port maintenance and management as well as general maritime safety. Modern numerical wind-wave modeling calculates the physical processes involved in wave generation, interaction, propagation, and dissipation, requiring large amounts of computational power to timely complete the required calculations. Convolutional Neural Networks (CNNs) have shown to improve wave prediction in viewpoints of lesser computational cost and processing speed. This study examines the application of Xception deep learning architecture for wave predictions along Japanese coasts of the Sea of Japan with input features of the Japan Meteorological Agency's Grid Point Value Meso-Scale Model (GPV-MSM). In particular, this study aims to verify the forecast skill for a testing period of one year and to understand how wave forecasts by deep learning models can be improved and utilized.

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Section: Verification Methodsmentioning

confidence: 99%