Tidal level prediction using combined methods of Harmonic Analysis and Deep Neural Networks in Southern Coastline of Iran

authored by

Kourosh Shahryarinia, MohammadAli Sharifi, Saeed Farzaneh

Abstract

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.

Organisation(s)

Geodetic Institute

External Organisation(s)

University of Tehran

Type

Article

Journal

Marine Geodesy

Volume

45

Pages

645-669

No. of pages

25

ISSN

0149-0419

Publication date

2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Oceanography

Sustainable Development Goals

SDG 14 - Life Below Water

Electronic version(s)

https://doi.org/10.1080/01490419.2022.2116615 (Access: Closed)

 

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