A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan
學年 109
學期 1
出版（發表）日期 2020-09-01
作品名稱 A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan
作品名稱（其他語言）
著者 Xi Feng; Gangfeng Ma; Shih-Feng Su; Chenfu Huang; Maura K.Boswell; Pengfei Xue
單位
出版者
著錄名稱、卷期、頁數 Ocean Engineering 211, 107526
摘要 A machine learning framework based on a multi-layer perceptron (MLP) algorithm was established and applied to wave forecasting in Lake Michigan. The MLP model showed desirable performance in forecasting wave characteristics, including significant wave heights and peak wave periods, considering both wind and ice cover on wave generation. The structure of the MLP regressor was optimized by a cross-validated parameter search technique and consisted of two hidden layers with 300 neurons in each hidden layer. The MLP model was trained and validated using the wave simulations from a physics-based SWAN wave model for the period 2005–2014 and tested for wave prediction by using NOAA buoy data from 2015. Sensitivity tests on hyperparameters and regularization techniques were conducted to demonstrate the robustness of the model. The MLP model was computationally efficient and capable of predicting characteristic wave conditions with accuracy comparable to that of the SWAN model. It was demonstrated that this machine learning approach could forecast wave conditions in 1/20,000th to 1/10,000th of the computational time necessary to run the physics-based model. This magnitude of acceleration could enable efficient wave predictions of extremely large scales in time and space.
關鍵字 Lake Michigan;Machine learning;Multi-layer perceptron;Wave forecasting
語言 en
ISSN 1873-5258
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
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