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Journal ArticleDOI

Neural networks in ocean engineering

Pooja Jain, +1 more
- 01 Jan 2006 - 
- Vol. 1, Iss: 1, pp 25-35
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TLDR
It is found that neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods.
Abstract
The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the research studies reported so far in these areas. It is found that, in general, neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods. The relative advantages of the neural network schemes proposed by various investigators are improved accuracy, lesser complexity in modeling and hence smaller computational effort and time, reduced data requirement in some cases, and so on. Neural networks have a very high degree of freedom, and that comes as handy while training it with examples. Exploration of more areas of application, implementation of advanced and hybrid forms of networks together with interpretation of the inf...

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Citations
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Renewable energy: Present research and future scope of Artificial Intelligence

TL;DR: In this paper, the authors summarized the review of reviews and the state-of-the-art research outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy.
Journal ArticleDOI

Comparison between m5 model tree and neural networks for prediction of significant wave height in lake superior

TL;DR: Model trees as a new soft computing method was invoked for prediction of significant wave height and error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate.
Journal ArticleDOI

Prediction of significant wave height using regressive support vector machines

TL;DR: Comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required, which shows that SVM can be successfully used for prediction of Hs.
Journal ArticleDOI

Hindcasting of wave parameters using different soft computing methods

TL;DR: This paper presents alternative hindcast models based on Artificial Neural Networks, Fuzzy Inference System (FIS) and Adaptive-Network-based FuzzY Inference system (ANFIS), which indicated that error statistics of soft computing models were similar, while ANFIS models were marginally more accurate than FIS and ANNs models.
Journal ArticleDOI

A novel model to predict significant wave height based on long short-term memory network

TL;DR: The simulating waves nearshore-LSTM (SWAN-L STM) model was proposed to make a single-point prediction, and it outperformed the standard SWAN model with an improvement in accuracy of over 65%.
References
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Journal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

TL;DR: In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Book

Advanced methods in neural computing

TL;DR: Advanced Methods in Neural Computing meets the reference needs of electronics engineers, control systems engineers, programmers, and others in scientific disciplines by explaining diverse high-performance paradigms for artificial neural networks that function effectively in real-world situations.
Journal ArticleDOI

Neural Networks for River Flow Prediction

TL;DR: This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor in the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich.