Short-term prediction of wave height based on a deep learning autoregressive integrated moving average model

Ban, Wenchao; Shen, Lei; Chen, Jiachao; Yang, Bin

doi:10.1007/s12145-023-01023-6

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…(2) SARIMA model: Seasonal Autoregressive Integrated Moving Average model add periodic treatment on the unstable data. The main idea came from observing the periodicity of data, receive the length of data(s), Season autoregressive order(P, the value is determined by PACF), Value seasonal moving average order(Q, the value is determined by ACF), Order of seasonal difference(D, usually the value is 0 or 1), and then we have a model of seasonality, the SARIMA model [6] .this paper constructs the SARIMA model for prediction, Build a model:SARIMA(p, d, q)(P, D, Q)m .The mathematical expression is:…”

Section: Data Description and Visualizationmentioning

confidence: 99%

Prediction of global mean temperature based on SARIMA combined model

Li,

Chu,

Gao

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87.

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Section: Data Description and Visualizationmentioning

confidence: 99%

Prediction of global mean temperature based on SARIMA combined model

Li,

Chu,

Gao

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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“…Later, Zhang et al [33] predicted the elastic modulus of compressive strength of standard high-strength concrete by the LSBoost method, with better prediction performance compared to traditional empirical equations and statistical regression methods. Furthermore, the Back Propagation (BP) neural network was successfully adopted to predict short-term effective wave height [34], demonstrating its nonlinear mapping ability, self-learning ability, and specific generalization and generalization abilities [35]. These advantages would help the BP neural network better predict wave height distribution, which has not been conducted in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Occurrence Probability of Freak Waves in Unidirectional Sea State Using Deep Learning

Zhou,

Wang,

Ding

et al. 2023

JMSE

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Predicting extreme waves can foresee the hydrodynamic environment of marine engineering, critical for avoiding disaster risks. Till now, there are barely any available models that can rapidly and accurately predict the occurrence probability of freak waves in a given state. This paper develops a trained model based on the Back Propagation (BP) neural network, with wave parameters of unidirectional sea state fed into the model, such as significant wave height, wave period, spectral type, and the intermodal distance of the peak frequencies. A rapid and accurate model optimized for predicting the occurrence probability of freak waves in a unidirectional sea state, from unimodal to bimodal configuration, is achieved by iterating to reduce accumulation errors. Compared to the regression and least-squares boosting trees, the optimized model performs much better in accurately predicting the occurrence probability of freak waves. Irrespective of whether in unimodal or bimodal sea state, this optimized model is competitive in calculation accuracy compared to theoretical models such as Rayleigh prediction and MER prediction, improved by at least 41%. The established model based on the BP neural network can quickly predict the threshold of freak waves in a given sea state, guiding practical engineering applications.

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