Prediction models for tidal level including strong meteorologic effects using a neural network

Liang, Shuxiu; Li, M.C.; Sun, Zhixin

doi:10.1016/j.oceaneng.2007.12.006

Cited by 55 publications

(16 citation statements)

References 21 publications

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“…Statistical errors of the training set were calculated for the different number of nodes in the hidden layer. Liang and Sun [35] proposed that number of hidden neurons should be half size of the input nodes plus output nodes for better forecasting accuracy. According to this guide, the numbers of hidden nodes within 39 were tested considering the size of input nodes for the most complex ANN structure treated in the present study.…”

Section: Ann Model Developmentmentioning

confidence: 99%

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Sung

Lee

Chung

et al. 2017

Water

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This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this river, an enhanced tributary water level forecasting model is proposed by adding multiple water level data on the main river as input variables into the conventional ANN structure which often uses rainfall and upstream water level data. Four types of ANN models per each lead-time are built with increasing complexity of the input vector, and their performances are compared. The results indicate that the inclusion of multiple water level data on the main river to the network provides water level forecasts with greater accuracy at the Ogeumgyo gauging station of interest. The final best ANN models for water level forecasts with lead-times of 1 to 2 h show good performance with root mean square errors (RMSE) below 0.06 m and 0.12 m, respectively. However, the final best ANN model for forecasting 3 h ahead was unsatisfactory, showing underestimation at many rising parts of the hydrograph.

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Section: Ann Model Developmentmentioning

confidence: 99%

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Sung

Lee

Chung

et al. 2017

Water

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“…It represents the influences on tide caused by celestial bodies and coastal topography. However, changes of tidal level are not only influenced by celestial bodies and coastal topography, but also by meteorological factors such as atmospheric pressure, wind, ice and rainfall [3]. These factors are both nonlinear and time-varying in nature, thus their influences on tidal change are complex and hard to be represented by strictly founded model.…”

Section: Introductionmentioning

confidence: 99%

“…Among those intelligent techniques, artificial neural network (ANN) can learn and represent complex nonlinear mapping underlying measured data adaptively, and stores the knowledge within computational neurons and connecting weights [5]. ANN has also been implemented in prediction of tides attributing to its natures such as inherent nonlinearity, Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom universal approximation capability and parallel information processing mechanism [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

Yin¹,

Wang²

2015

Proceedings in Adaptation, Learning and Optimization

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a b s t r a c tA novel sampling pool selection scheme is proposed for the online sequential extreme learning machine (OS-ELM) based on improved Gath-Geva (IGG) fuzzy segmentation algorithm. Tidal change is a timevarying process whose dynamics vary with changes of internal and environmental factors such as celestial bodies movements, coastal topology and environmental disturbances. When OS-ELM is implemented for identifying time-varying system dynamics, it usually sequentially selects samples with fixed number. Under such circumstance, samples representing different system dynamics are mixed together so that the online representation and prediction abilities of OS-ELM may be deteriorated. To consciously select samples with most representing ability and construct appropriate sampling pool for OS-ELM, in this study, a dynamic sampling pool selection scheme is proposed based on IGG fuzzy segmentation approach. Time series of input and output variables are segmented as per their dynamics characteristics. The change points split up the time series into several segments and the change points themselves represent the changes of system dynamics. Samples within the same segment are considered as possessing homogeneous characteristics. To achieve best representing abilities for current system dynamics, the proposed IGG-based sampling scheme is implemented for selecting sampling pool. The OS-ELM selects homogeneous samples from sampling pool thus possesses better representing ability for current dynamics. In the meantime, conventional harmonic analysis is also applied to represent the influences of celestial bodies and coastal topology. The harmonic method and IGG-based OS-ELM are combined together and the resulted modular prediction scheme is applied for online tidal level prediction of ports of King Point, Mokuoloe and Old Port Tampa in the United States. Simulation results demonstrate the feasibility and effectiveness of the proposed sampling scheme and the modular tidal prediction approach.

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“…Rajasekaran et al (2006) construct functional networks and sequential learning neural networks based on history tidal observations; Pashova and Popova (2011) daily mean sea level prediction. Huang et al (2003) proposed regional neural network for water level (RNN-WL) prediction method by taking use of tidal level data of stations distributed in regional scale; Lee (2004) employed tidal constituents for generating long-term predictions; Chang and Lin (2006) incorporated the factors related to tide-generating forces in the neural network; Lee and Jeng (2002) considered the residual fluctuation in the network's input layer for prediction purposes; Altunkaynak (2012) employed current wind speed and the previous significant wave height as inputs to give predictions of current significant wave height; Liang et al (2008) incorporated wind information in the neural tidal prediction model, such as wind speed, wind direction, and day-averaged wind speed. Herman et al (2007) utilized the principle components of tide, together with the wind components and historical tidal data, for predictions of tidal level and currents.…”

Section: Introductionmentioning

confidence: 99%

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

Yin

Wang

2015

Engineering Applications of Artificial Intelligence

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keywords:Tidal prediction Hybrid model Variable neural network Harmonic method Sliding data window a b s t r a c t Accurate real time tidal prediction is essential for human activities in coastal and marine fields. Tidal changes are influenced not only by periodic revolutions of celestial bodies but also by time-varying meteorological factors. For accurate real-time tidal prediction, a hybrid prediction mechanism is constructed by taking both advantages of harmonic analysis and neural network. In the proposed mechanism, conventional harmonic analysis is employed for representing the influences of celestial factors; and neural network is used for representing the nonlinear influences of meteorological factors. Furthermore, to represent time-varying tidal dynamics influenced by meteorological factors, a variable neural network is real-time constructed with the neurons and the connecting parameters are adaptively adjusted based on a sliding data window (SDW). The hybrid prediction method uses only the latest short-period data to generate predictions sequentially. Hourly tidal data measured at four American tidal stations are used to validate the effectiveness of the hybrid sequential tidal prediction model. Simulation results of tidal prediction demonstrate that the proposed model can generate accurate short-term prediction of tidal levels at very low computational cost.

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Prediction models for tidal level including strong meteorologic effects using a neural network

Cited by 55 publications

References 21 publications

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

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