Improving wave predictions with artificial neural networks

Makarynskyy, Oleg

doi:10.1016/j.oceaneng.2003.05.003

Cited by 216 publications

(65 citation statements)

References 9 publications

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“…However, computational cost increases exponentially with increasing order of complexity, such as increasing the number of hidden layers [52]. Previous studies using ANN algorithm to establish forecast models suggest that a three-layer structure is capable of generating satisfactory correlations among input and output datasets [22,55,56]. Deo [1] summarized four steps to mathematically obtain outputs using ANN algorithms.…”

Section: Hl(j)mentioning

confidence: 99%

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Ren

Hartnett

2018

Remote Sensing

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Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach-Artificial Neural Networks (ANN)-was applied to predict surface currents in a tide-and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation (≥0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model's peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind-and tide-dominated currents using ANN.

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Section: Hl(j)mentioning

confidence: 99%

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Ren

Hartnett

2018

Remote Sensing

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“…Artificial Neural Networks have been used for real time wave forecasting [8], improving wave predictions [9], the hindcasting of storm waves [10] and estimating ocean wave parameters from wave spectra [11]. Understanding the interrelationship between wave parameters has also been investigated [12].…”

Section: B Previous Workmentioning

confidence: 99%

Wave height quantification using land based seismic data with grammatical evolution

Donne

Nicolau

Bean

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Accurate, real time, continuous ocean wave height measurements are required for the initialisation of ocean wave forecast models, model hindcasting, and climate studies. These measurements are usually obtained using in situ ocean buoys or by satellite altimetry, but are sometimes incomplete due to instrument failure or routine network upgrades. In such situations, a reliable gap filling technique is desirable to provide a continuous and accurate ocean wave field record. Recorded on a land based seismic network are continuous seismic signals known as microseisms. These microseisms are generated by the interactions of ocean waves and will be used in the estimation of ocean wave heights. Grammatical Evolution is applied in this study to generate symbolic models that best estimate ocean wave height from terrestrial seismic data, and the best model is validated against an Artificial Neural Network. Both models are tested over a five month period of 2013, and an analysis of the results obtained indicates that the approach is robust and that it is possible to estimate ocean wave heights from land based seismic data.

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“…Since the principle of ANN has been well documented in the literature, only a brief explanation is given in this section. The feasibility of three-layer for the reproduction of complex system behaviour was proved empirically by a number of applications (Huang et al 2003, Lee 2004, Makarynskyy et al 2004, Pierini and Gómez 2009.…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)

et al. 2013

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A b s t r a c tIn the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.

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Improving wave predictions with artificial neural networks

Cited by 216 publications

References 9 publications

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Wave height quantification using land based seismic data with grammatical evolution

Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)

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