Details
| Original language | English |
|---|---|
| Pages (from-to) | 645-669 |
| Number of pages | 25 |
| Journal | Marine Geodesy |
| Volume | 45 |
| Issue number | 6 |
| Early online date | 28 Aug 2022 |
| Publication status | Published - 2022 |
Abstract
Keywords
- CNN, Harmonic analysis, LSTM, MLP, tidal level prediction
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Oceanography
Sustainable Development Goals
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In: Marine Geodesy, Vol. 45, No. 6, 2022, p. 645-669.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Tidal level prediction using combined methods of Harmonic Analysis and Deep Neural Networks in Southern Coastline of Iran
AU - Shahryarinia, Kourosh
A2 - Sharifi, MohammadAli
A2 - Farzaneh, Saeed
PY - 2022
Y1 - 2022
N2 - Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.
AB - Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.
KW - CNN
KW - Harmonic analysis
KW - LSTM
KW - MLP
KW - tidal level prediction
UR - http://www.scopus.com/inward/record.url?scp=85136891041&partnerID=8YFLogxK
U2 - 10.1080/01490419.2022.2116615
DO - 10.1080/01490419.2022.2116615
M3 - Article
VL - 45
SP - 645
EP - 669
JO - Marine Geodesy
JF - Marine Geodesy
SN - 0149-0419
IS - 6
ER -