Forecasting tidal currents from tidal levels using genetic algorithm

Remya, P. G.; Kumar, Raj; Basu, Samik

doi:10.1016/j.oceaneng.2011.12.002

Cited by 23 publications

(12 citation statements)

References 15 publications

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“…In [18] genetic algorithms (GA), were used to carry out the prediction task. A preliminary empirical orthogonal function (EOF) analysis was used to compress the spatial variability into a few eigen-modes, so that GA could be applied to the time series of the dominant principal components (PC).…”

Section: Previous Research For Tidal Currents Forecastingmentioning

confidence: 99%

A Proposed Algorithms for Tidal in-Stream Speed Model

Aly

El-Hawary

2013

AJEE

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Abstract:In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.

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Section: Previous Research For Tidal Currents Forecastingmentioning

confidence: 99%

A Proposed Algorithms for Tidal in-Stream Speed Model

Aly

El-Hawary

2013

AJEE

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“…Liang, Li, and Sun (2008) developed three Back-Propagation Neural Network models in order to improve the accuracy of prediction and supplement of tidal records. Pashova and Popova (2011) applied different Artificial Neural Networks (ANNs) (multilayer Feed-Forward (FF), Cascade-Feed-Forward (CFF), Feed-Forward Time-Delay (FFTD), Radial Basis Function (RBF), Generalized Regression (GR) neural networks and Multiple Linear regression (MLR) methods) for tidal prediction model at the town of Burgas in the western Bulgarian Black Sea coast during 1990-2003. Remya, Kumar, and Basu (2012 applied a preliminary empirical orthogonal function (EOF) analysis in order to compress the spatial variability into a few eigenmodes, so that Genetic Algorithm (GA) could be applied to the time series of the dominant principal components (PC).…”

Section: Introductionmentioning

confidence: 99%

Application of Fluvial-12 model for calculation of maximum deformation in cross sections of tidal rivers (the Karun River in Iran)

2019

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In tidal rivers design return period is multiplication of return periods of floods and tidal flows. This research considers the down stream of the Karun River in southwest of Iran as case study. For calibration of Fluvial-12 model, the deformations of river cross sections from 1996 to 2013 were measured by surveying. Using of the Ackers -White (1973) equation eventuates the best fitness between calculated deformations by Fluvial-12 model and observed deformations. This equation is an appropriate sediment transport equation for river beds and banks that their materials are silt or fine sand such as the most of tidal rivers in the world. For design return period N years, the highest erosion occurs at flood return period N years and tidal flow return period 1 year. At cross sections near the mouth of the river the highest sedimentation occurs at flood return period 1 year and tidal flow return period N years but at cross sections far from the mouth of the river the highest sedimentation occurs at flood return period N years and tidal flow return period 1 year. Because sedimentation occurs at flood cycle of tide at near the mouth of the river, tidal flow that its velocity is low causes sedimentation. On the other hand, erosion occurs at ebb cycle of tide and fluvial flow intensifies ebb velocity toward the mouth of the river therefore fluvial flow causes erosion at the whole length of the river.

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“…Another advantage is that it provides an explicit analytical forecast equation. [12] The predictive skill of GA has been demonstrated in the cases of sea surface temperature (SST) in the Alboran Sea, [13] summer rainfall over India, [14] SST and sea level anomaly in the Ligurian Sea, [15] wave heights in the north Indian Ocean (NIO), [3,4] the tidal currents in the Arabian Sea, [16] and so on. Meanwhile, ocean surface wind prediction with in situ and scatterometer observations using GA have been tested in the NIO and the results show that predictions with GA made up to three days have been found to be quite encouraging.…”

Section: Introductionmentioning

confidence: 99%

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

Zhong¹,

Dong²,

Yimei³

et al. 2016

Chinese Phys. B

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The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.

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Forecasting tidal currents from tidal levels using genetic algorithm

Cited by 23 publications

References 15 publications

A Proposed Algorithms for Tidal in-Stream Speed Model

A Proposed Algorithms for Tidal in-Stream Speed Model

Application of Fluvial-12 model for calculation of maximum deformation in cross sections of tidal rivers (the Karun River in Iran)

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

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