Tidal level forecasting using functional and sequential learning neural networks

Rajasekaran, S.; Thiruvenkatasamy, K.; Lee, Tsong Lin

doi:10.1016/j.apm.2005.03.020

Cited by 32 publications

(11 citation statements)

References 20 publications

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“…[5] and [6] used 5 most influencing harmonic constituents (which they found using a no-hidden layer ANN) in a 3 layered feed forward neural network to supplement tidal data when it is missing as well as to predict tidal levels one year in advance with 15 days of hourly tidal observations. [7] developed a functional network and the sequential learning neural network for accurate prediction of tides during surge using short-term observation. Based on 34-day observed data, functional network model predicted the time series data of hourly tides directly, using an efficient learning process that minimizes the error.…”

Section: Introductionmentioning

confidence: 99%

Towards predicting water levels using artificial neural networks

Londhe¹

2009

Oceans 2009-Europe

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For all Ocean related activities it is necessary to predict the actual water levels as accurate as possible. The present work aims at predicting the water levels with a lead time of few hours to a day using the technique of artificial neural networks. Instead of using the previous and current values of observed water level time series directly as input and output the water level anomaly (difference between the observed water level and harmonically predicted tidal level) is calculated for each hour and the ANN model is developed using this time series. The network predicted anomaly is then added to harmonic tidal level to predict the water levels. The models exhibit a high level of prediction accuracy in testing. The models were further tested in real time mode for 8 months as well as during hurricane conditions. The model results were highly in agreement with the observed water levels in both the cases.I.

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Section: Introductionmentioning

confidence: 99%

Towards predicting water levels using artificial neural networks

Londhe¹

2009

Oceans 2009-Europe

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“…In [31], the approach proposed by Castillo et al [32], combining finite differences (FDs) and FNs, was used for predicting the tidal level. An extended version of this approach was proposed in [26] for the quantitative analysis in a gas sensing system.…”

Section: Functional Networkmentioning

confidence: 99%

Fitted Q-iteration by Functional Networks for control problems

Gaeta

Loia

Miranda

et al. 2016

Applied Mathematical Modelling

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In this paper a new offline model-free approximate Q-iteration is proposed. Following the idea of Fitted Q-iteration, we use a computational scheme based on Functional Networks, which have been proved to be a powerful alternative to Neural Networks, because they do not require a large number of training samples. We state a condition for the convergence of the proposed technique and we apply it to three classical control problems, namely, a DC motor, a pendulum swing up, a robotic arm. We present a comparative study to show the approximation capabilities of our method with a relatively small number of training samples

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“…His method computed the large matrix of the error if the layers and neurons of the network increase. Rajasekaran, Thiruvenkatasamy, and Lee (2006) developed functional and sequential learning neural networks to predict tidal level with a typhoon surge effect.…”

Section: Introductionmentioning

confidence: 99%

Selection of Methods for Predicting Tidal Levels with a Typhoon Surge Effect

Yuan

Tan

Wang

2015

Journal of Coastal Research

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Yuan, H.; Tan, M., and Wang, W., 2015. Selection of methods for predicting tidal levels with a typhoon surge effect.Accurate prediction of tidal level with strong meteorological effects is very important for human activities in oceanic and coastal areas. Under severe weather, such as typhoons, the contribution of non-astronomical components to tidal level may be as significant as that of astronomical components. The traditional harmonic analysis method and other models based on the analysis of astronomical components do not work well in these situations. The objective of this paper is to select an effective method for predicting the tidal level under the influence of typhoons at Luchaogang, China. The linear regression, back-propagation (BP) and radial basis function neural network methods are compared. Based on the typhoon data and tidal-level data gathered from Pudong Hydrological Bureau, seven parameters of typhoon are extracted and employed as input parameters while the corresponding tidal-level data are adopted as output parameters. The results have shown that the BP method performs best in the prediction of tidal level with a typhoon effect, and that the BP neural network method can provide a solid basis for tidal-level prediction and typhoon disaster reduction. ADDITIONAL INDEX WORDS:Back-propagation neural network, radial basis function, linear regression, tidal-level prediction.

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Tidal level forecasting using functional and sequential learning neural networks

Cited by 32 publications

References 20 publications

Towards predicting water levels using artificial neural networks

Towards predicting water levels using artificial neural networks

Fitted Q-iteration by Functional Networks for control problems

Selection of Methods for Predicting Tidal Levels with a Typhoon Surge Effect

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