Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan

Lee, Tsung-Lin

doi:10.1016/j.engappai.2007.03.002

Cited by 106 publications

(41 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In function of the network architecture, there are several types of neural networks. The neural networks are used in applications as various as varied: in [2], a neural network has been used to predict energy consumption in a residential sector; in [9] a in [9] a neural network has been used to predict storms in the short term; in [11], the rain assessment network starting from radar measurements.…”

Section: Introductionmentioning

confidence: 99%

A neural network based approach for wind resource and wind generators production assessment

et al. 2010

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A neural network based approach for wind resource and wind generators production assessment

et al. 2010

View full text Add to dashboard Cite

“…Researchers use Artificial Neural Network (ANN) for prediction of different natural hazards (Lee & Liu 2000;Huang et al 2003;Lui et al 2006;Mishra et al 2007;Pradhan & Lee 2007;Lee 2008;Adeli & Panakkat 2009). Although ANN gives reasonable performance, it has various limitations such as black box approach, arriving at local minima, low generalization capability, overfitting, etc.…”

Section: Introductionmentioning

confidence: 99%

Determination of stability of epimetamorphic rock slope using Minimax Probability Machine

Kumar¹,

Samui

Naithani³

2014

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

The article employs Minimax Probability Machine (MPM) for the prediction of the stability status of epimetamorphic rock slope. The MPM gives a worst-case bound on the probability of misclassification of future data points. Bulk density (d), height (H), inclination (b), cohesion (c) and internal friction angle (f) have been used as input of the MPM. This study uses the MPM as a classification technique. Two models {Linear Minimax Probability Machine (LMPM) and Kernelized Minimax Probability Machine (KMPM)} have been developed. The generalization capability of the developed models has been checked by a case study. The experimental results demonstrate that MPM-based approaches are promising tools for the prediction of the stability status of epimetamorphic rock slope.

show abstract

“…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. Sufficient information is more crucial for predictions of abnormal tidal changes under violent meteorological conditions, such as strong winds and sudden changes in atmospheric pressure (Lee, 2008). Tseng et al (2007) used typhoon's characteristics, local meteorological conditions and typhoon surges at a considered tidal station as inputs for typhoon surges forecast; Lee (2006Lee ( , 2008 incorporated wind information and atmospheric pressure in the model for storm surge prediction.…”

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

View full text Add to dashboard Cite

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.

show abstract

Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan

Cited by 106 publications

References 18 publications

A neural network based approach for wind resource and wind generators production assessment

A neural network based approach for wind resource and wind generators production assessment

Determination of stability of epimetamorphic rock slope using Minimax Probability Machine

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

Contact Info

Product

Resources

About