Forecasting of Time Series Significant Wave Height Using Wavelet Decomposed Neural Network-Reference-Cited by-同舟云学术

Forecasting of Time Series Significant Wave Height Using Wavelet Decomposed Neural Network

Published:2015 Issue: Volume:4 Page:540-547
ISSN:2214-241X
Container-title:Aquatic Procedia
language:en
Short-container-title:Aquatic Procedia

Author:

Prahlada R.,Deka Paresh Chandra

Publisher

Elsevier BV

Reference11 articles.

1. Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea;Chen;Ocean Engineering,2007

2. Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time;Deka;Ocean Engineering,2012

3. Khandekar, S.D., Deka, P.C., 2013. Multistep lead time forecasting of hydrologic time series using daubechies wavelet – neural network hybrid model. International Journal of Scientific & Engineering Research., vol 4, ISSN 2229-5518.

4. Kisi, O., Neural networks and wavelet conjunction model for intermittent streamflow forecasting. ASCE Journal of hydrology 14, 773-782.

5. Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting;Krishna;ASCE J. Hydrologic Engg.,2014

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Harnessing LSTM and XGBoost algorithms for storm prediction;Scientific Reports;2024-05-18

2. Artificial neural networks ensemble methodology to predict significant wave height;Ocean Engineering;2024-05

3. Using statistical and machine learning approaches to describe estuarine tidal dynamics;Journal of Hydroinformatics;2024-03-13

4. Dynamic adaptive wavelet based fuzzy framework for extended significant wave height forecasting;Ocean Engineering;2024-03

5. Short-Term Prediction of the Intermediate Point Temperature of a Supercritical Unit Based on the EEMD–LSTM Method;Energies;2024-02-18