A hybrid approach to estimate the nearshore wave characteristics in the Persian Gulf

Salah, Parastoo; Reisi-Dehkordi, Ashkan; Kamranzad, Bahareh

doi:10.1016/j.apor.2016.02.005

Cited by 19 publications

(2 citation statements)

References 21 publications

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“…Complex numerical models, based on spectral approaches or Boussinesq or the mild slope equations, are usually used to define the wave climate in the proximity and within harbour areas. However, such models imply high computational costs and are unfit for forecasting and nowcasting systems (Salah et al, 2016). Artificial Intelligence algorithms, such as Artificial Neural Networks (ANNs), can be adopted to overcome this limit.…”

Section: Introductionmentioning

confidence: 99%

Wind and Wave Trained Artificial Neural Networks for the Forecasting of Wave Climate in Harbour Area

Cavallaro,

Iuppa,

Castro

et al. 2023

Int. Conf. Coastal. Eng.

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Nowadays, maritime transportation has expanded rapidly, involving the need to enhance several navigation-related issues, particularly concerning the safety of navigation, which is significantly impacted by weather conditions. In this regard, creating a wave forecasting system could facilitate vessel movement at the harbour entrance or inside the sheltered area. Wave characteristics are usually estimated using numerical models, which generally require high computational costs, making them inadequate for nowcasting and forecasting wave climate. The current study describes the implementation of a forecasting methodology for the port area of Augusta (Sicily) based on an Artificial Neural Network (ANN) that attempts to deliver a trustworthy response and the numerical model but with a significant reduction in the computational time.

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Section: Introductionmentioning

confidence: 99%

Wind and Wave Trained Artificial Neural Networks for the Forecasting of Wave Climate in Harbour Area

Cavallaro,

Iuppa,

Castro

et al. 2023

Int. Conf. Coastal. Eng.

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“…In general, the application of ML techniques in this field has been focused on providing forecasts, particularly for the growing renewable energy sector (Hatalis et al (2014), Cornejo-Bueno et al (2016). Increasingly more complex ML approaches are being applied as different techniques are leveraged to improve performance (Salah et al (2016)) or facilitate more widescale applicability (Pirhooshyaran and Snyder (2020)).…”

Section: Introductionmentioning

confidence: 99%

Investigating Machine Learning for Virtual Wave Monitoring

Peach¹,

Carthwright²,

Strauss³

2020

Int. Conf. Coastal. Eng.

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Wave monitoring is a time consuming and costly endeavour which, despite best e orts, can be subject to occasional periods of missing data. This paper investigates the application of machine learning to create "virtual" wave height (Hs), period (Tz) and direction (Dp) parameters. Two supervised machine learning algorithms were applied using long term wave parameter datasets sourced from four wave monitoring stations in relatively close geographic proximity. The machine learning algorithms demonstrated reasonable performance for some parameters through testing, with Hs performing best overall followed closely by Tz; Dp was the most challenging to predict and performed relatively the poorest. The creation of such "virtual" wave monitoring stations could be used to hindcast wave conditions, fill observation gaps or extend data beyond that collected by the physical instrument.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/GM3EG2_SQa0

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