An artificial neural network based system for wave height prediction

We present a system for predicting the hourly significant wave height at a specific wave measurement station in the middle of Israel's Mediterranean coast (Hadera). Our system uses an artificial neural network (ANN) composed of two sub-networks. We evaluate the importance of different inputs to...

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Bibliographic Details
Published inCoastal engineering journal Vol. 65; no. 2; pp. 309 - 324
Main Authors Dakar, Elad, Fernández Jaramillo, José Manuel, Gertman, Isaac, Mayerle, Roberto, Goldman, Ron
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.04.2023
Taylor & Francis Inc
Subjects
Artificial intelligence
Artificial neural network
Artificial neural networks
Atmospheric models
Dimensions
Mathematical analysis
Mathematical models
Measuring instruments
Neural networks
Permutations
Predictions
Significant wave height
Spatial data
Spatial filtering
Tensors
Wave data
Wave forecast
Wave height
Wave measurement
Wind
Wind data
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Summary:We present a system for predicting the hourly significant wave height at a specific wave measurement station in the middle of Israel's Mediterranean coast (Hadera). Our system uses an artificial neural network (ANN) composed of two sub-networks. We evaluate the importance of different inputs to the system. The input includes wind forecast data from the SKIRON atmospheric modeling system, wave forecast for the station's location given by the SWAN wave model, and observed wave data. Our system pre-processes the wind data using a spatial filtering scheme and then enters it into the first sub-network in the form of a multidimensional tensor. We take special care to interconnect the tensor elements through a dimensional permutation that leads the ANN to sum elements along all the tensor's dimensions. Our system groups the output of the first sub-network with the rest of the input and feeds it to the second sub-network that gives the prediction. Our ANN system outperforms the SWAN wave model in estimating wave heights over 1.5 meters. We obtain the best performance when either all input components are used or just wind and observations. Reimplementation of the system at Ashkelon yields smaller improvements due to insufficient training data.
ISSN:2166-4250
1793-6292
DOI:10.1080/21664250.2023.2190002