Journal ArticleDOI
Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks
TLDR
A machine learning model based on Long Short-Term Memory neural networks for reconstruction and short- and long-term prediction of nearshore significant wave height (SWH), integrating bathymetric data for the first time is developed.About:
This article is published in Ocean Engineering.The article was published on 2021-07-15. It has received 55 citations till now. The article focuses on the topics: Sea state & Significant wave height.read more
Citations
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Journal ArticleDOI
A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction
Irene Karijadi,Shuo-Yan Chou +1 more
TL;DR: In this article , a hybrid method of Random Forest (RF) and Long Short-Term Memory (LSTM) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was proposed to predict building energy consumption.
Journal ArticleDOI
Review of the application of Artificial Neural Networks in ocean engineering
TL;DR: Artificial Neural Networks (ANNs) were firstly used to model ocean engineering problems in the decade of 1990s as discussed by the authors , and since then, this soft-modeling technique has proved several advantages against traditional approaches.
Journal ArticleDOI
The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method.
TL;DR: Wang et al. as mentioned in this paper proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTMs under the limited data.
Journal ArticleDOI
Reconstruction of nearshore wave fields based on physics-informed neural networks
TL;DR: In this article , a physics-informed neural network (PINN) is used to model nearshore wave transformation and the performance of the PINN is compared with numerical solutions from XBeach and experimental data over a two-dimensional alongshore uniform barred beach and a three-dimensional circular shoal.
Journal ArticleDOI
An auto-encoder based LSTM model for prediction of ambient noise levels
TL;DR: In this paper , a deep learning model based on Auto-encoder infused with Long short-term memory (LSTM) was proposed to predict ambient noise levels in Indian cities, which can inherit non-stationary characteristics of time-series data while considering non-linear pattern.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.