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 -