A novel framework for significant wave height prediction based on Adaptive Feature Extraction Time-Frequency Network
Authors:
- Jianxin ZhangJZJianxin Zhang
- Lianzi JiangLJLianzi Jiang
- Xinyu HanXHXinyu Han
- Xiangrong WangXWXiangrong Wang
Abstract
No abstract availableFunding Information
- Natural Science Foundation of Shandong Province (ZR2023QA090, ZR2019ZD42)
- China Postdoctoral Science Foundation (2024M761844)
- National Natural Science Foundation of China (52201339)
This publication has 49 references indexed in Scilit:
- Design and modeling of wave energy converter glider (WEC-Glider) with simulation validation in wave tank experimentsApplied Energy, 2024
- Hybrid deep learning model for wave height prediction in Australia's wave energy regionApplied Soft Computing, 2023
- A hybrid model for multistep-ahead significant wave height prediction using an innovative decomposition–reconstruction framework and E-GRUApplied Ocean Research, 2023
- A multi-objective approach for location and layout optimization of wave energy convertersApplied Energy, 2023
- Dynamic ensemble deep echo state network for significant wave height forecastingApplied Energy, 2022
- Case Study of a Short-Term Wave Energy Forecasting Scheme: North Indian OceanJournal of Ocean University of China, 2021
- Sea spray aerosol fluxes in the Baltic Sea region: Comparison of the WAM model with measurementsEstuarine, Coastal and Shelf Science, 2017
- Assessment of wave energy resource of the Black Sea based on 15-year numerical hindcast dataApplied Energy, 2013
- Quantifying the storm erosion hazard for coastal planningCoastal Engineering, 2009
- Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network modelsCoastal Engineering, 2007